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What information do you need to implement your research plan and answer your research questions, and what do you not need? Plan the collection and processing of personal data so that you only collect the information you need. This way, you fulfill of the data protection regulation.

Think lifecycle-wise: how long during your research will you need direct and indirect personal data (e.g., original recordings, transcripts, field notes, analysis data, contact information of subjects, pseudonymization code list)? Data protection legislation requires that data be retained by the researcher only for the justified period stated in the research plan.

When drafting the data protection notice, ensure you understand what pseudonymization and anonymization mean in practice. This way, you can confidently deliver on what you promise to your subjects. If it is not possible to pseudonymize qualitative data (e.g., oral history research, ethnography) due to its nature and content, describe in the data protection notice that personal data will be processed directly identifiable for a justified reason.

Prepare data collection so that only the necessary information is recorded in the data you collect. See examples below.

Allocate time in your calendar to implement the pseudonymization and anonymization measures you have planned. If necessary, agree on the division of labor with the research team members well in advance.

Examples: The variables of a structured survey can, where possible, be constructed as categorized or generalized questions instead of detailed values. For example, age can be grouped (35-40 years old, 40-45 years old), and the place of residence can be asked at the regional level instead of the exact municipality if municipal data is not needed for the analysis.

Pre-interview questions can be structured in written form and asked, for example, as background information at the necessary level, categorized or generalized as in the example above.

It can be agreed with the interviewee to avoid particularly sensitive details or additional comments that are not specifically intended to be addressed in the interview.

If sensitive information that is not needed accumulates in the data, it is good practice to cut it from the recording and leave it untranscribed.

Pseudonymisation is a process in which direct identifiers and, if necessary, strong indirect identifiers are removed from analysis data files.

Strong indirect identifiers are pieces of information from which an individual can likely be identified by the context in the data or/and by comparing the information in the data with other information of the person available in other sources, even if the data does not contain direct identifiers (for example, a rare disease, municipality of residence, and job title combined).

Proper names can be replaced with code identifiers (e.g., [R01H02]) or pseudonyms (e.g., [Mira]). The list for reversing pseudonymisation is kept separate from the analysis files for as long as it is necessary to retain direct identifiers for the research. 

Options for keeping the identifiable information separate include:

1. The reversal list file can be stored in a different JYU storage system than the analysis data itself, for example, in CollabRoom or in a folder created on a network drive if the analysis files are stored in Nextcloud.
2. The list can also be encrypted with the Cryptomator encryption software, allowing it to be stored in the same storage system as the analysis data files. See instructions forand

Pseudonymisation means that, in addition to direct identifiers, so-called strong indirect identifiers are removed from the analysis data as a data protection measure. These identifiers do not directly allow the identification of a person but, when combined with each other or with publicly available information about the person, are likely to lead to identification (for example, a rare profession, gender, place of residence, and years of service together). Strong indirect identifiers are typically found in open-ended responses to surveys and writings, as well as in interview responses.

Example: A survey about working life quality among seasonal workers of Asian background in Northern Finland collects quantitative and qualitative data. The qualitative data contains free-text responses where the respondents account details of their life situations, and their backgrounds. After transctiption, original video recordings are disposed of in a secure way, leaving the transctiptions and field notes in digital form. 

In the open-ended responses, the respondents tell intimately about their family situations with names, their social and occupational background, their philosophical views on life, beliefs, and their health issues. Even though direct identifiers such as names are removed from the text and the original recordings disposed of, the information is still rich and detailed enough to be counted as strongly indirectly identifiable, therefore not pseudonymous but identifiable. Pseudonymisation would mean that the open-ended answers are modified by e.g. coarsening, classification, and omission, to blur likely indirectly identifiable bits of information. in the texts.

Anonymised data no longer contains personal data, i.e. it is not possible to identify a single living person with reasonable effort from the direct or indirect information contained in the data. The GDPR does not apply to anonymised data.

How do I ensure that my data is anonymous? 

• All files containing direct identifiers, such as the original interview recordings, consent forms, de-pseudonymisation code list, and the contact information of the subjects have been destroyed
• Indirect identifiers that may appear in open-ended answers and writings, as well as in interview answers, have been blurred, for example by roughing, classifying or, if necessary, removing details from the data

Examples: 

Coarsening: The variable, 'municipality' is broadened to 'region', as in Helsinki to the Southern Uusimaa, Finland.  

Classification: Age is changed into an age group, or height or weight into categories: 14 years to '10 to 15 years', 87 kilograms to 80 to 90 kilograms.

Deletion: a completely unique, identifiable expression is deleted from the data and the deletion is marked in the text with, for example, two square brackets and a hyphen: [-]. 

Document your planned measures in your data management plan.