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Research data management

Research Data Management (RDM) aims to systematically organise, secure, and document scientific data throughout its entire lifecycle.

Support at the ZHAW

ZHAW Services Research Data Services team offers advice, training, and direct support to ZHAW members for specific challenges in research data management. This includes assistance with data management planning, utilisation of specific tools or programming languages, addressing technical and legal aspects (e.g., data anonymisation or encryption), as well as guidance on data publication and archiving.

About us

The ZHAW Research Data Services team comprises members from three organisational units: the University Library, the R&D department, and ICT. Our areas of activity include infrastructure, tools and support. 

Our Offer

We provide a selection of 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 or alternatively contact us at researchdata@zhaw.ch.

Support for Researchers from Researchers

Since 2023, the ZHAW Services Research Data team has been additionally supported by researchers from various departments. This support is part of the project DSembedded; Integrating discipline-specific Know-How into Data Stewardship, which is co-funded by swissuniversities. Researchers with proven experience in RDM are engaged in support activities. In this role, they identify new trends and impulses in their research fields and adjacent disciplines that lead to implications and demands on RDM. Furthermore, they provide support, upon request, for data management or other data-specific processes of research projects. The aim of this two-year pilot project is to customize the services as much as possible to the individual and discipline-specific needs of researchers and to develop customised solutions. Additionally, it aims to promote best practices in research data handling and integrate them sustainably into everyday research practices.

Department Contact Area of Expertise and special knowledge
School of Health Sciences Michelle Haas Specialised knowledge in quantitative methods, specialised knowledge in the use of REDCap, programming skills in Matlab and R as well as publication of health data.
School of Health Sciences Patricia Schwärzler Expertise in qualitative methodology
School of Applied Linguistics Klaus Rothenhäusler Specialised knowledge in the field of automatic language processing, linguistic processing pipelines and corpus provision. Support with the publication of language data on LaRS.
School of Life Sciences and Facility Management Nils Ratnaweera Specialised knowledge in the field of spatial (geo) data, Linux systems, literate programming/computational notebooks, Git and GitHub, Python, R, Bash, web systems (HTML/CSS/JS), Shiny applications, and data visualisation.
School of Applied Psychology Pirmin Pfammatter Specialised knowledge in quantitative research, programming skills in R, artificial intelligence in the research data process. Editorial board member of forschungsdaten.info.
School of Social Work Rainer Gabriel Expertise in statistical data analysis with R, questionnaire programming with LimeSurvey and RedCap, as well as data publication on SwissUBase.
School of Social Work Lorenz Biberstein Expertise in statistical data analysis with R, questionnaire programming with LimeSurvey and RedCap, as well as data publication on SwissUBase.
School of Engineering Reto Bürgin Specialised knowledge in methods of statistical data analysis, with a focus on evaluation methods for surveys. Programming skills in R. Employee of the Institute of Data Analysis and Process Design (IDP).
School of Engineering Nima Riahi Specialised knowledge in Data Science inquiries, data applications, data pipelines, and reproducibility. Programming skills in R and Python. Employee of the Institute of Data Analysis and Process Design (IDP).
School of Management and Law Tibor Pimentel Specialised expertise in anonymization and publication of data, as well as management of health data. Programming skills in R.

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.