Hello, I am a Data Steward
Research Software Engineering and Data Stewardship Career Talk
Lars Schöbitz
Prof. Elizabeth Tilley
June 17, 2025
Meet a data steward
Meet a data steward
I have:
- 10+ years work experience (5 in research, at Eawag)
- empathy, compassion, patience, persistence
- an affinity for IT
- teaching experience
- learned how people learn
I don’t have:
- a doctoral degree
- a qualification in computer science
- a qualification in statistics
- a lot of time
Job Description: Open Science Specialist
8 learnings from 4 years
#1 Technology is not on our side
The Modern Academic’s Challenges
- Overflowing email inboxes
- Browsers with hundreds of tabs
- Files on stored on Desktops
- MS Teams, Slack, Element, NAS, Google Drive, …
- Credentials, Passwords, OTPs, 2FAs, PATs, …
#2 ETH wants reproducibility
ETH RDM Guidelines
ETH RDM Guidelines: https://rechtssammlung.sp.ethz.ch/Dokumente/414.2en.pdf
FAIR data sharing principles
Article accessible at: https://www.nature.com/articles/sdata201618
FAIR data sharing principles
- Technical in nature
- Require data management strategy to establish workflows
- Not a checkbox, but a process
Findable
Accessible
Interoperable
Reusable
#3 Data management is project management
Access anonymised data from ghedata
GHE Student Wiki (public)
- Grading criteria
- Communication expectations
- Data storage and data management guidelines
- Presentation standards
- Proposal and thesis writing requirements
Transparent grading practices and clear expectations offer several advantages:
Improved student performance: When students understand what is expected of them, they are better equipped to meet those expectations.
Reduced anxiety: Clear guidelines help alleviate student stress and uncertainty about assignments and evaluations.
Fairness: Transparent grading criteria ensure that all students are evaluated consistently and equitably.
Enhanced learning: Students can focus on learning objectives rather than guessing what the supervisor wants.
Grading rubric & data publication
Four areas of evaluation with 31 sub-areas
- 40/100: Research competence
- 40/100: Thesis report
- 10/100: Colloquium
- 10/100: Examination
‘Data Management’ under ‘Research Competence’
6: Data is fully documented, organized, easy to reproduce, and publication ready. Everything is stored on Google Drive.
But, data publication requirement
Obtaining a 6 from all sub-areas but not publishing the data in the form of a repository will result in a maximum allowed grade of 5.75.
Our Grading rubrics
#4 Low IT affinity is not a lack of aptitude
Safe learning environments
Growth-mindset for better learning outcomes
- Fixed mindset: ‘I’m not good’
- Growth mindset: ‘I can learn’
Create safe learner environments
- Regular 1:1 research data management meetings
- Bi-monthly half day team events
- Yearly retreat
Access our Strategy & Planning
Course website FS25: https://rbtl-fs25.github.io/website/
#5 Data != Data
Disclaimer: Data at GHE
- small (few MBs)
- tabular
- non-sensitive
- topics
- waste management
- sanitation
- air quality
- etc.
Three terms for three stages
Three terms for three stages
| term | explanation | file format |
|---|---|---|
| unprocessed raw data | data that is not processed and remains in its original form and file type | often XLSX, also CSV and others |
Three terms for three stages
| term | explanation | file format |
|---|---|---|
| unprocessed raw data | data that is not processed and remains in its original form and file type | often XLSX, also CSV and others |
| processed analysis-ready data | data that is processed to prepare for an analysis and is exported in its new form as a new file | CSV, R data package |
Three terms for three stages
| term | explanation | file format |
|---|---|---|
| unprocessed raw data | data that is not processed and remains in its original form and file type | often XLSX, also CSV and others |
| processed analysis-ready data | data that is processed to prepare for an analysis and is exported in its new form as a new file | CSV, R data package |
| final data underlying a publication | data that is the result of an analysis (e.g descriptive statistics or data visualization) and shown in a publication, but then also exported in its new form as a new file | CSV |
#6 Data management is a process, not a checkbox
#7 Findable: Publish for humans and computers
Automation from ETH Research Collection
Automation from Zenodo
Automation from GitHub
Open Source
made for collaboration
Automation from GitHub
made for humans
#8 Funding for Open Research Data exists existed
Funding schemes
swissuniversities
- 2021 - 2024: swissuniversities - Open Science I
- 2025 -
20282026: swissuniversities - Open Science II - Watch: Action Line B5.2 - Professionalisation of ORD specialists and related services
- Newsletter sign-up: https://sympa.ethz.ch/sympa/subscribe/isci
Funding schemes
Open Research Data Program of the ETH Board
- 2021 - 2024: ~ 96 projects funded (~ CHF 15 million budget in total)
- Global Health Engineering was awarded 2 Contribute and 3 Explore projects worth 500’000 CHF
- All 96 projects and newsletter sign-up: https://open-research-data-portal.ch/ (bottom of page)
Data from: Massari, Schöbitz, and Tilley (2025)
8 take-aways from 30 minutes
- #1 Technology is not on our side
- #2 ETH wants reproducibility
- #3 Data management is project management
- #4 Low IT affinity is not a lack of aptitude
- #5 Data != Data
- #6 Data management is a process, not a checkbox
- #7 Findable: Publish for humans and computers
- #8 Funding for Open Research Data
existsexisted
Thanks! 🌻
Slides created via revealjs and Quarto: https://quarto.org/docs/presentations/revealjs/
Slide background image taken from Danielle Navarro
Access slides as PDF on GitHub or on https://ghe-open.ch/slides/
All material is licensed under Creative Commons Attribution Share Alike 4.0 International.
References
Massari, Nicolo, Lars Schöbitz, and Elizabeth Tilley. 2025. “Ethord: ETH Board Open Research Data (ORD) Program Project Metadata and Report Data.” https://doi.org/10.5281/zenodo.15554776.