Ethics and Open Science
In order to engage with Open Science practices, you must consider some ethical issues that apply to your work. There are three key considerations:
- Being clear with participants at the time of collection about what you plan to use the research data for
Make sure that your participant information sheet and consent form inform participants that their anonymised data will be made available to the general public via the internet. Participants should consent to their anonymised data being freely shared. Not providing this information technically precludes the sharing of data files with others in any form. Therefore, language such as below should be included in the participant information sheet and consent form:
Information sheet
Because the data we collect from you may be of interest to other researchers, we will publish it on a publicly accessible online data repository. At that point, anyone will have access to your data. However, this only concerns fully anonymised information that cannot identify you, and we will only do this with your permission (please confirm this on the consent form).
Consent form
I understand that data from this study may be of interest to other researchers. I consent for my non-identifiable data to be shared through a publicly accessible online data repository. I know that even if I do not consent to this, I can still participate.
If the dataset contains any identifying information, you should also specify how participants can withdraw their consent for sharing this data at any time in the future:
I know I can opt out of this at any time in the future by contacting xxxx, quoting my personal ID number.
- Anonymity of data and protecting the identity of your participants
If data cannot be anonymized and shared, a solution can be sharing metadata (a summary of the data, such as group means, standard deviations, effect sizes, a table with zero-order correlations between all study variables, etc.). Metadata should be richly described and contain accurate and relevant attributes. Clear indication of who generated the metadata should also be included. Signposting for data that is available by request only may also be appropriate.
Data can be anonymous – with no identifiers – or confidential – with some information that can identify individuals which should be kept according to data protection legislation. All data should be kept according to the permissions we have sought and been granted by the participants or data custodians.
When data is confidential, such that it has some clearly identifiable information (e.g., addresses, names, postcodes, places, or other identifiers), much of this can be removed before data deposit or replaced with a unique identifier in the case of data that is linked over time. Where data requires anonymising before deposit, two individuals should independently inspect the dataset to remove any identifiable information or recode information in a standardised way as to minimise the risk of identification. Particular care should be given to the following:
Information including names, addresses, demographic information, place names, specific instances, dates of birth, etc.: remove or systematically recode.
Textual information which may refer to or identify information or people: consider appropriate recoding which keeps meaning without identifying a person or situation.
Numeric information which has a frequency of less than five which may identify an individual: consider collapsing categories down, depending on whether it is meaningful, or recode to a blank other.
For anonymous data that do not include any identifying information as standard, data should be checked for low frequency items which may need to be recoded as other or open questions should also be inspected for identifying information regarding self or others by one person before deposit.
- Respecting the wishes of those who take part in research
A commonly used phrasing is “Be as open as possible, as closed as necessary”. Individuals should not be excluded from research participation if they would not like their information shared.
For a more extensive treatment of ethical considerations and practical tips regarding data sharing, see Meyer (2018).