Practical Tools for Personal and Sensitive Research Data
A table curated by the Personal Data Support Network (PDSN) node of the Swiss Research Data Support Network (SRDSN)
Abstract
This page shows a download of a community-maintained, curated table of practical software tools that researchers can use to manipulate personal or sensitive data throughout the research lifecycle. It was produced by members of Work Group 1 (WG1) of the Personal Data Support Network (PDSN) node, within the Swiss Research Data Support Network (SRDSN): https://www.researchdatasupport.ch/. The original resource is published on Zenodo
Authors: Simon van Rekum 
, ZAHW; Francesco Varrato , EPFL.
Contributors: Sebastian Perrig , FHNW; Gorka Fraga Gonzalez , UZH.
NoteClick for details about the tables
Practical Tools for Personal and Sensitive Research Data — Comparative Table
Description
This resource is a community-maintained comparative table of practical software tools that researchers can use to manipulate personal or sensitive data throughout the research lifecycle. It was produced by members of Work Group 1 (WG1) of the Personal Data Support Network (PDSN) node, within the Swiss Research Data Support Network (SRDSN) (researchdatasupport.ch).
The table is intended to help researchers, data stewards, and support staff at Swiss higher education institutions (HEIs) identify suitable tools for handling personal or sensitive research data in a privacy-respecting and legally compliant manner.
Scope: The table covers only tools that researchers actively use (or can use) to manipulate data — e.g., to collect, transcribe, pseudonymize, anonymize, encrypt, share, or archive it. It does not include guides on compliance procedures nor training material.
IMPORTANT: Authors and contributors do not assume responsibility for its accuracy, completeness, or currency. Users are advised to verify tool-specific information — especially regarding legal compliance and data protection — before making decisions.
What Is Covered
The table is organized into thematic sheets, each addressing a category of tools for personal/sensitive data treatment:
| Sheet | Purpose |
|---|---|
| Collection | Survey and interview tools suitable for sensitive data collection |
| Transcribe | Audio transcription tools with privacy-preserving features |
| Pseudo & Anonymize | Tools for pseudonymization, anonymization, and re-identification risk assessment |
| Share | Secure platforms for sharing sensitive research data |
| Encrypt | Encryption tools for files, folders, drives, and cloud storage |
| Others | Additional tools (e.g., synthetic data generation, differential privacy platforms) |
Column Descriptions
Each tool entry is described along the following dimensions:
- TOOL w/ LINK — Name of the tool, linked to its main webpage.
- MAIN FUNCTION — General, short description of the tool’s primary function with respect to personal/sensitive research data handling.
- USE CASE — Short example(s) of typical research use.
- OPEN SOURCE — Whether the tool’s source code is publicly available:
- ✅ Y — fully open source
- ❌ N — proprietary
- ✅❌ only app — open-source application, but not the underlying backend
- ◧ Partial — partially open source
- NERD DEGREE — Technical complexity required to use the tool:
- ⚙️ Low — User-friendly GUI, minimal technical knowledge required
- ⚙️⚙️ Medium — Some technical understanding helpful, moderate learning curve
- ⚙️⚙️⚙️ High — Requires technical expertise, command-line or programming knowledge
- ⚙️⚙️⚙️⚙️ Very High — Advanced technical skills required (R/Python, algorithms, infrastructure)
- DATA TYPE — Types of research data the tool can read, process, or export (format-oriented, not discipline-oriented).
- DEPLOYMENT — How/where the tool is deployed:
- fully on-prem (offline)
- on-prem (networked, no vendor data transfer)
- Swiss cloud (vendor-managed SaaS)
- foreign cloud (self-managed)
- foreign cloud (vendor-managed SaaS)
- hybrid (local client + vendor cloud)
- AI BASED — Whether the tool uses AI/ML:
- No ML/AI
- Yes – traditional AI (rules, NER, etc.)
- Yes – modern AI/LLM or deep learning
- Unknown / not stated
File Naming and Versioning
The filename Practical_Tools_YYYYMMDD contains the date of the most recent contribution. Contributors are expected to update this date (and the contributor list below) with each new contribution. This README follows the same naming convention.
Contributions and Disclaimer
The contributors to this table work under a best-effort assumption: information is provided in good faith and to the best of contributors’ knowledge at the time of writing. Contributors do not assume any responsibility for the accuracy, completeness, or currency of the information. Users are advised to verify tool-specific information — especially regarding legal compliance, data protection, and institutional availability — before making decisions.
Main Maintainers
- Francesco Varrato — EPFL
- Simon van Rekum — ZHAW
Contributors (as of 2026-06-10)
- Gorka Fraga Gonzalez — UZH
- Sebastian Perrig — FHNW
Files in This Dataset
| Filename | Format | Description |
|---|---|---|
Practical_Tools_20260610.xlsx |
XLSX | Comparative table (Microsoft Excel, proprietary format) |
Practical_Tools_20260610.ods |
ODS | Comparative table (OpenDocument Spreadsheet, open format) |
README_20260610.md |
MD | This README file |
License
This table and this README are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
You are free to share and adapt this material for any purpose, provided appropriate credit is given, a link to the license is provided, and any changes are indicated.
🔗 Full license text: https://creativecommons.org/licenses/by/4.0/
How to Cite
If you use or build upon this resource, please cite it as:
van Rekum, S., & Varrato, F. (2026). Practical Tools for Personal and Sensitive Research Data - Curated Table (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.20543121
When citing a specific release, users should prefer the version-specific DOI provided by Zenodo.
Contact
For questions, corrections, or contributions, please contact the main maintainers or contact the SRDSN via this form https://www.researchdatasupport.ch/index.php/contact.