Information on data protection

• The promotion of early career researchers is one of the strategic principles of UZH. According to § 4 of the University Act («UniG» – currently only available in German), UZH is required to ensure quality in research and teaching.
• The pilot project «Quality Assurance of PhD Supervision» contributes to a closed quality cycle in doctoral supervision by introducing a standardized feedback mechanism.
• Data is collected, evaluated, and communicated following the applicable data protection regulations of the Canton of Zurich and UZH. Technical and organizational security measures are implemented to protect the data.

More detailed information can be found in the privacy policy (PDF, 220 KB).

1. Purpose of data collection: The collection of general and personal survey data serves to ensure and further develop the quality of doctoral supervision at UZH.
2. Data collection: The survey is conducted using the LimeSurvey tool. The data is processed within the University of Zurich and is not transferred to external parties. A technical access token is used to manage invitations and reminders. It only serves to track participation status and is not linked to the anonymous responses.
3. Data analysis: The information provided by doctoral candidates remains anonymous, ensuring that no conclusions can be drawn about individuals.
   The personal data of supervisors is processed in encrypted form and analyzed exclusively in aggregated form. An analysis at the level of individual supervisors is only carried out if at least ten responses have been received or the five-year moratorium has been reached.
4. Data storage: The data is stored in encrypted form on UZH servers and is only accessible to authorized persons. Data management is the responsibility of the Graduate Campus. 
5. Data transfer: Aggregated results are made available to the deans within the framework of internal quality processes. At the level of individual supervisors,aggregated result reportsare addressed exclusively by the respective deanswithin the framework of regular leadership discussions.Deans do not have access to raw data.
6. Security measures: Technical and organizational measures are in place to protect the data.

• Use of feedback: The survey is not intended to affect individual supervision relationships but serves the overarching purpose of institutional quality development.

• Collection, administration and analysis: The data is collected, administered, and analyzed centrally by the Graduate Campus.

• No access for supervisors: Supervisors do not have access to any individual survey data at any time.

• Aggregation of feedback: Feedback from doctoral candidates is aggregated to preserve anonymity and to prevent any individual responses from being traced.

• No disclosure of personal data: No personal data of participating doctoral candidates is disclosed.

• Protection against identification: The data protection measures in place serve to prevent the identification of individual doctoral candidates.

• Use of feedback: Feedback is included in institutional quality development. It is not interpreted as an individual assessment but considered within the overall institutional context.

• Collection, administration and analysis: The data is collected, administered and analyzed centrally by the Graduate Campus.

• Role-based access: Access to results is governed by role-based permissions. Aggregated data relating to individual supervisors is addressed exclusively by the deans within the framework of regular leadership discussions; supervisors do not receive direct access to survey data.

• Encryption of personal data: The personal data of supervisors is processed in encrypted form after collection. A review at the level of individual supervisors only takes place if at least ten responses are available or the five-year moratorium has been reached.

• Aggregation of feedback: Feedback is evaluated only once a sufficient data basis is available, in order to prevent biased interpretation and avoid conclusions being drawn from individual cases.

• Protection against identification and bias: The applied data protection measures protect against identification and reduce the risk of biased interpretation.