Please select one of these combinations of workshops, i.e. minimum two workshops. It is also possible to attend all three workshops, which will increase the workload during the Summer School.

Workshop 1 - Version Control with Git

Workshop 2 - Introduction to Research Data Management with openBIS

For information on the workshop content, please check the abstracts for the workshops before registration. The abstracts are available on the Summer School website: Link

Workshop 1 - Version Control with Git

Workshop 3 - Research Data Management with Renku – A platform for reproducible and collaborative data analysis

For information on the workshop content, please check the abstracts for the workshops before registration. The abstracts are available on the Summer School website: Link

Workshop 2 - Introduction to Research Data Management with openBIS

Workshop 3 - Research Data Management with Renku – A platform for reproducible and collaborative data analysis

For information on the workshop content, please check the abstracts for the workshops before registration. The abstracts are available on the Summer School website: Link

Workshop 1 - Version Control with Git

Workshop 2 - Introduction to Research Data Management with openBIS

Workshop 3 - Research Data Management with Renku – A platform for reproducible and collaborative data analysis

For information on the workshop content, please check the abstracts for the workshops before registration. The abstracts are available on the Summer School website: Link

1. Content 

In this workshop, participants will get the chance to explore existing datasets from different disciplines. After an introduction on important steps that one should take ideally before publishing a research dataset, participants will be asked to work in groups on different datasets. Participants will evaluate the quality of these datasets and will assess whether all relevant criteria for data publishing are met. Based on the analysis of existing datasets, groups of participants will be guided through steps for, e.g., cleaning up the dataset, selection of relevant data for publication, and for choosing a suitable FAIR data repository.

2. Focus of the workshop 

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

3. 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

4. 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.

5. Prerequisites

No prior knowledge is necessary to participate. The workshop is suitable for all summer school participants.

1. Content

As the expectations of researchers increase, publishing reproducible scientific articles becomes essential. However, choosing tools for these tasks can be difficult. This course aims to guide researchers through these challenges by introducing a workflow that utilizes the Quarto scientific and technical publishing system for collaborative scientific writing. At the end of the workshop, every student will have a personal, published, free website that links to their (academic) profiles, and has the potential to be used as a (scientific) blog. The workshop will be taught using participatory live coding as a teaching method.

2. Focus of the workshop

This workshop focuses on tools for collaborative scientific writing within the context of reproducible documents, combining data analyses with code and narrative text in the same place. The concept is titled literate programming, an aspect of research data management that is gaining attention. While we don’t teach a programming language, the provided tools allow you to integrate programming languages such as R or Python within your writing.

3. Learning objectives

This course 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, and tables
• Learn to use Quarto Pub to publish a website and share research with a broader audience

4. Target audience

Prior experience in a programming language is not required for this course, 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.

5. Prerequisites

Prior to the course, learners are expected to have worked through the pre-course assignments listed on the course website: https://quarto-rdmss-24.github.io/website/