ETH Research Data Management Summer School 2026

Git is a version control system for managing changes in code over time and coordinating software development within teams. It is a fundamental tool in modern software development. 

If your answer to any of the following questions is “yes”, then working with Git might be of interest to you: 

• Some of my code files have dates or versions encoded, like “script_abc_from_july_2020.py”.
• I want to undo a change I made to my code, e.g. several weeks ago. 
• I want to compare my current code with a previous version because it stopped working. 
• I work on the same code with others.
• I want to publish code on public code repositories, such as github.com or gitlab.com. 

Focus of the workshop

The workshop will introduce basic principles, the most useful commands and best practices for working with Git and GitLab/GitHub. It also includes an introduction to working with your computer’s command line/terminal. 

Learning objectives

• Understanding Git basic principles, what it does, and how and when to use it. 
• How to publish work on public repositories such as gitlab.com or github.com

Target audience

Researchers who work with programming code or text files.

Prerequisites

No previous knowledge required.

external page openBIS is a combined data management platform, electronic laboratory notebook, and inventory management system. openBIS helps scientists to meet requirements from of funding agencies, journals and academic institutions for publishing data in line with the FAIR data principles. Scientists can use openBIS to document their daily experimental or computational work, store any related experimental raw and derived data, and link everything to materials, samples and protocols stored in the lab inventory. The system ensures safe data storage and provenance tracking.

openBIS is an open source software distributed under the Apache v2.0 licence, developed by ETH Zurich Scientific IT Services (ETH SIS). ETH SIS provides services based on openBIS to research groups at ETH Zurich (https://ethz.ch/staffnet/en/it-services/catalogue/software-business-applications/research-data-management.html) and at other external page Swiss academic institutions.

Learning objectives

In this workshop, we will provide a general overview of openBIS followed by a practical training session.

At the end of the workshop, the participants are expected to be able to:

• use the openBIS inventory of materials and samples;
• use the openBIS inventory of laboratory protocols;
• document projects and experiments in the openBIS lab notebook and enter data.

Target audience

Experimental scientists working in research labs.

Prerequisites

• Knowledge/competencies
  This workshop is designed for beginners and does not require any previous knowledge.
• Technical equipment
  Participants are required to have their own laptop. An openBIS test server will be made available to participants on the day of the workshop. Recommended web browsers are Firefox, Safari or Chrome.
  
  The course material will be provided a few days before the workshop and participants will be required to download the material onto their laptops and have it ready for use on the day of the workshop. 

Want to follow best practices in data management but don’t know where to start? Renku’s data management toolkit helps you manage every aspect of your project - data, code, workflows and computational environments, so you can rest assured that someone else (or even yourself six months down the line) will be able to find, understand and reuse your research. Renku is a platform for reproducible and collaborative data analysis. It provides single-click access to containerised, interactive computational environments without prior knowledge of underlying technologies.

Renku is based on modular project configurations for bootstrapping your project with a mix of code repositories, data sources, and computational environments and evolve them as the project matures. For example, in an early stage of a project, you may simply require a quick way to open a dataset in a small compute environment for some basic data exploration; as the analysis of the data deepens, you may need to add a code repository to start versioning the code and collaborate with others in a more structured way. Later on, someone else on the project might build a containerised app or dashboard to demonstrate the data properties and model results more easily with non-experts. Renku allows you to select the components that you need for your project to work.

In addition to connecting all the required resources under one roof, Renku makes these elements searchable, encourages their reuse between projects, and illuminates connections between them, even across projects.

Focus of the workshop

In this workshop, you will learn how to use Renku to take advantage of Open Science best practices from the start.

Learning objectives

At the end of the workshop, the participants are expected to be able to:

• set up and use Renku to manage data, code and computational environments in a reusable fashion,
• use the platform to seamlessly share data, code, and computational environments with others.

Target audience

Researchers that develop and run, or want to develop and run, computational workflows for simulations, processing and analysing their data.

Prerequisites

No previous knowledge required.

In this workshop, participants will have the opportunity to explore a variety of existing datasets from different disciplines. Following an introduction to important steps that should ideally be taken before publishing a research dataset, participants will work in groups on different datasets. They will evaluate the quality of these datasets and assess whether all the relevant criteria for data publishing are met. Based on the analysis of existing datasets, the groups will be guided through steps such as cleaning up the dataset, selecting relevant data for publication, and choosing a suitable FAIR data repository. 

Focus of the workshop

Designed in an interactive format, this workshop offers initial guidance followed by hands-on group exercises. Participants will learn how to evaluate the readiness of datasets for publication, enhance dataset structure and value, and navigate considerations prior to publishing their own datasets. 

Learning objectives

Participants will be able to:

• list the criteria to assess the publication-readiness of datasets, 
• critically evaluate the publication-readiness of datasets from different disciplines, 
• enhance dataset value based on predefined criteria, 
• list and reflect on relevant criteria for selecting a suitable FAIR data repository for a certain dataset, 
• independently select suitable FAIR data repositories for their datasets. 

Target audience

This workshop caters to doctoral students, postdocs, and data stewards who seek guidance in publishing their datasets effectively. It is particularly beneficial for individuals responsible for datasets yet to be published. 

Prerequisites

No previous knowledge required.

As expectations placed on researchers increase, publishing reproducible scientific articles becomes essential. However, choosing tools for these tasks can be challenging. This workshop aims to guide researchers through these challenges by introducing a workflow that utilises the Quarto scientific and technical publishing system for collaborative scientific writing. At the end of the workshop, each participant will have a personal, published, free website that links to their (academic) profiles, and can be used as a (scientific) blog. The workshop will use participatory live coding as a teaching method. 

Focus of the workshop

This workshop focuses on tools for collaborative scientific writing in the context of reproducible documents, combining data analyses with code and narrative text in one place. This concept is known as literate programming, which is an aspect of research data management that is gaining increasing attention. While no programming language is taught, the tools provided allow you to integrate programming languages such as R or Python into your writing. 

Learning objectives

This workshop has the following learning objectives: 

• Learn to use the Quarto file format and the RStudio IDE visual editing mode to produce scholarly documents with footnotes, cross-references, figures/graphs, and tables. 
• Learn to use Quarto Pub to publish a website and share research with a broader audience. 

Target audience

Prior experience in a programming language is not required for this workshop, but learners who have worked with data science tools such as R, RStudio IDE, Jupyter Notebooks, Python, or VS Code may have an advantage and gain the most from the material. 

Prerequisites

Prior to the workshop, learners are expected to have worked through the pre-workshop assignments listed external page on the workshop website.