Philipp Gies

Dr Philipp Gies

Data Manager (for the Social Sciences)

Department: Research Infrastructure

Group: Research Data Service

Phone: +49 (0)421 23800 - 28

Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Office: Fahrenheitstr. 6, Annexe
28359 Bremen

Room: 2204 (2nd floor)

Research interests

As Data Manager for the Social Sciences at ZMT, I support researchers in planning, organising, documenting, archiving and reusing social science research data. My work is situated at the interface of scientific practice, research data management and the development of research infrastructure. My aim is to make research data management a practical instrument that supports research from proposal development and data collection to analysis, archiving and reuse.
A particular focus of my work is the connection between the FAIR and CARE principles. FAIR refers to data that are findable, accessible, interoperable and reusable; CARE adds perspectives on collective benefit, responsibility, research ethics and equitable governance. This relationship is especially relevant in the social sciences, where data should be well documented and reusable, while also remaining sensitive to context, personal information, consent and ethical requirements.
I am increasingly interested in new digital methods, tools and AI-supported workflows, including OCR, web scraping, automated documentation processes and Python-based workflows. My central concern is how research data management can be made methodologically sound, technically feasible and useful for researchers in practice.
Responsibilities at ZMT
• Advising researchers on data management plans, data organisation, documentation, metadata and archiving.
• Supporting FAIR-oriented research data management for social science data.
• Developing CARE-sensitive perspectives on governance, data protection, research ethics and responsible reuse.
• Advising on informed consent, anonymisation of qualitative interviews and secure data processing.
• Supporting qualitative data organisation and analysis, particularly with MAXQDA.
• Integrating social science data practices into inter- and transdisciplinary research projects.
• Acting as contact person for Qualiservice regarding the long-term archiving of qualitative research data.
• Testing new workflows, tools and AI-supported procedures for practical research data management.

Methodological and Data Expertise

My methodological and data expertise combines social science research experience with practice-oriented research data management. I have experience with qualitative and quantitative methods, including open and standardised interviews, group interviews, qualitative content analysis, surveys and the use of MAXQDA.

In addition, I work on data management plans, anonymisation strategies, metadata concepts, preparation for archiving and scenarios for data reuse. I am also developing skills in Python-based and AI-supported methods, including OCR, web scraping, data preparation and documentation workflows.

Academic Background

My previous academic work focused on transnational social dialogue, multi-level governance and forms of statehood in transnational spaces. In my doctoral research, I examined how international framework agreements, employee representation and transnational exchange processes shape governance structures and social dynamics.

This research perspective also informs my current work in research data management. I am interested in how institutional rules, infrastructures and governance models shape scientific data practices, and how they can be developed further to support transparent, responsible and reusable research.

Links and Resources:

Languages:

German (native)
English (fluent)
Spanish (basic)