Research data management
Research Data Management (RDM) aims to systematically organise, secure, and document scientific data throughout its entire lifecycle.
Support at the ZHAW
The ZHAW Research Data Services team offers advice, training, and direct support to ZHAW members for specific challenges in research data management. The team comprises members from three organisational units: the University Library, the R&D department, and ICT.
Its areas of activity include data management planning, utilisation of specific tools or programming languages, addressing technical and legal aspects (such as data anonymisation or encryption), as well as guidance on data publication and archiving.
A selection of our offers
We provide training and consulting services
- IFP R / Python courses
- REDCap
- Git / Version Controlling
- Review of data before publication
- Review DMP
Additionally, we offer:
- Guidelines for data publication
- Checklist for data archiving
- FAQ on Legal Aspects of Research Data
- DMP template for SNF
ZHAW members can find further information on this offer on the Self-Service Portal (ZHAW login required) or alternatively contact us at researchdata@zhaw.ch.
ZHAW Research Data Panel
The ZHAW Services Research Data team works closely with the schools to offer suitable services in the area of research data management (RDM). As part of the project DSembedded: Integrating Discipline-Specific Know-How into Data Stewardship, co-financed by swissuniversities, this collaboration was further intensified.
Researchers from the schools with a track record in RDM participated in the development of services and support activities. After the project’s conclusion, the collaboration is continued in the form of an expert panel on research data. The panel members serve as contact persons within their schools. They review trends and developments in the disciplines and research communities that involve new requirements for RDM.
The goal of the panel is to tailor the services to the individual and discipline-specific needs of researchers. Additionally, best practices in handling research data are to be promoted and sustainably embedded in day-to-day research activities.
Contact persons within the schools
| School | Contact |
|---|---|
| School of Applied Linguistics | Klaus Rothenhäusler |
| School of Applied Psychology |
Andrea Frick Aureliano Crameri |
| School of Health Sciences | Michelle Haas |
| School of Health Sciences | Esther Ruf |
| School of Life Sciences and Facility Management | Stefan Glüge |
| School of Life Sciences and Facility Management | Nils Ratnaweera |
| School of Engineering | Andreas Binkert |
| School of Engineering | Reto Bürgin |
| School of Engineering | Nima Riahi |
| School of Management and Law | Matthias Hofer |
| School of Management and Law | Tibor Pimentel |
| School of Social Work | Rainer Gabriel |
| School of Social Work | Lorenz Biberstein |
FDM and Open Research Data
RDM aims to systematically organise, secure, and document scientific data throughout its entire lifecycle—from collection to archiving. This promotes transparency, reproducibility, and trust in research results. Additionally, it serves as a foundation for public sharing (Open Research Data) and the reuse of data for further analyses, as reference data, or for simulations and AI training.
In the course of the discussion surrounding Open Science, the topic of Open Research Data has also gained increasing importance. ZHAW is dedicated to promoting open science and open innovation through its R&D policy. Consequently, researchers are expected to make the data collected in their research projects publicly accessible whenever it is technically, legally, and ethically feasible.
This principle is increasingly advocated by foundations and public funders and formulated as a condition for project funding, while also financially supported: for instance, the Swiss National Science Foundation covers costs of up to CHF 10,000 for the preparation and publication of datasets.
Open Research Data is established to varying degrees in different disciplines. In disciplines such as bioinformatics, genomics, climate and geosciences, or public health, solid ecosystems of publicly accessible data have already been formed, providing significant value to the research communities.