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| dcc:pdpsol:datadesctruction [2026/01/29 14:56] – Included text marlon | dcc:pdpsol:datadesctruction [2026/02/24 09:24] (current) – [Final clean-up (after archiving)] marlon | ||
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| ====== Data Destruction ====== | ====== Data Destruction ====== | ||
| ===== Introduction ===== | ===== Introduction ===== | ||
| Although it is possible to prevent the collection of data, it is also important to think about the data that you might need for your research at first, but that become redundant later on. By planning for timely de-identification and destruction of your data, you can better protect your participants. You can use the examples below can help you prepare for the timely destruction of data. | Although it is possible to prevent the collection of data, it is also important to think about the data that you might need for your research at first, but that become redundant later on. By planning for timely de-identification and destruction of your data, you can better protect your participants. You can use the examples below can help you prepare for the timely destruction of data. | ||
| - | ===== Informed Consent forms ===== | + | ==== Secure and destroy personal identifiable notes ==== |
| - | Consent forms can reveal | + | During your research, you may unexpectedly need to record |
| - | ==== Online consent | + | ====Securely remove your data from devices and platforms |
| - | If you are conducting questionnaire research via an online platform (e.g., Qualtrics), you can ask consent via a question in the platform itself. Make sure to follow | + | While collecting data, it is often necessary to use storage solutions that are no longer necessary after you have collected |
| - | When asking for consent, ensure you collect only the personal data that is necessary: | + | ====Final clean-up (after |
| - | * If your objective is to collect anonymous or de-identified data, do not ask for names or other contact details for consent registration purposes. | + | Before |
| - | * If your objective is to collect identifiable or sensitive personal data, use a pseudonymization ID to prevent direct identification. At the relevant time in the project, remove the link between the consent and the participant’s identity reported in your keyfile. For example, when you've started to analyze the data and the participants can no longer request their data to be removed | + | |
| - | + | ||
| - | ==== Paper consent | + | |
| - | If you are conducting interviews or experiments, it is common practice | + | |
| - | + | ||
| - | When asking for consent, ensure you collect only the personal data that is necessary: | + | |
| - | * If your objective is to collect anonymous | + | |
| - | * If your objective is to collect (pseudonymized) personal | + | |
| - | + | ||
| - | After you finish your research, scan paper consent forms; | + | |
| - | + | ||
| - | ==== Audio consent ==== | + | |
| - | If you are conducting interviews, it is sometimes necessary | + | |
| - | + | ||
| - | Make sure the verbal consent recorded via audio or video is saved separately from your research data (e.g., experiment, interview, observation etc.); Archive the consent files in a separate location (separate folder with different access rights) from your research data; Be aware that audio or video recordings of informed consent cannot be fully anonymized without altering their content; use an extra layer of protection, such as [[https:// | + | |
| + | After you have confirmed that your data is archived correctly, you can critically review the data you have stored during your research: | ||
| + | * Check whether you have removed all copies of your data from secondary locations (e.g. the data collection platform, laptop, SurfDrive, Unishare, etc.) | ||
| + | * If you are no longer planning to use the data for future projects, consider also deleting it from your main working directory after archiving. Make sure to follow the agreements made with participants. | ||
| + | * If you are still working with the data, consider tidying your working directory: 1) Delete any temporary working files (e.g. data that can easily be regenerated by archived scripts). 2) Remove duplicate or obsolete versions (e.g. final_v1.csv, | ||
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