Renku Pilot Project is not designed for storing nor handling sensitive data (subject to strict rules and principles).
⚠ Don’t upload datasets larger than 1GB for the time being - you can find more about this limitation here .


You can contact the Science IT team via:

There are several channels provided by the Renku team - see here, e.g.,

  • for the general Renku documentation see here
  • for discussions about Renku please see the help Forum - "This is a good place to ask questions and find answers"
  • for a chat with the Renku team please use Gitter

Feedback [2022-09-02]

In the last 2 months we (Science IT) provided you with more information about RenkuLab.

Now we are conducting a short survey (≤5 minutes) in order to learn more about your experience with RenkuLab, your interest in such a potential service, and conclude if and how we will further investigate the establishment of a RenkuLab service at UZH.

To better interpret your answers with respect to your professional role, and to link it to the initial questionnaire, we do not record this survey anonymously.
Note that your data will be treated confidentially (known only to a few people inside Science IT). The conclusions and the anonymously aggregated supporting data from this survey might be presented to relevant stakeholders.

Please fill out the survey (≤5 minutes) at by Monday, the 12th of September 2022, using the UZH email address.

Use Cases [2022-07-18]

The Introduction and the Tutorial provide a good basis to reflect on how Renku can help address different use cases. You will find some comments and additional information related to the original use cases that triggered your interest in the Renku Pilot Project:

  • I am an RStudio or JupyterLab user
    You can create a project using the desired R or Python template. You can define the desired packages using requirements.txt for Python or install.packages for R. Starting the session you will be able to use RStudio or JupyterLab.
  • I want to improve the computational reproducibility
    RenkuLab enforces several best practices by using state-of-the-art tools for computational reproducibility: the versioning of the code (via git), the management of data (via git Large File Storage) - read more at Data in Renku, and the computational environment (container image). Moreover, Renku can be used to 1) import data from data repositories, e.g., Zenodo, 2) create new datasets and annotate them with metadata - read more at Create Renku dataset crate; 3) version and export them to Zenodo - read more at Dataset versioning. The workflow can be automated using the renku command-line client, which ultimately leads to the full provenance - read more at  Provenance of results. The relationship between data and code is reformatted into a knowledge graph that will provide enhanced search functionality - read more at The Renku Knowledge Graph.
  • I want to easily share my computational projects
    One should distinguish between the visibility level: private or public projects, and the ultimate aim: to rerun, to use it as a template for your own work, or to contribute to it. The public projects can be rerun using an anonymous session (available on In case you want to use a public project as a template you can fork it. The forked project might be used to make a merge request into the original project - read more about this at Project forking workflow. If you would like to allow people to contribute or even make changes to a public or private project, you should add them as members; e.g., Developer or Maintainer privileges. Read the Collaborating on Renku
  • I want to easily link my data, code, and computational environments
    RenkuLab is doing this by default. The code is versioned using git, and for each git commit there is a linked computational environment (i.e., the corresponding container image, and data). Renku can import data from repositories like Dataverse or Zenodo, and it uses git Large File Storage (LFS) to handle this process - read Data in Renku. Moreover, Renku has the advanced option of mounting S3 buckets - read Mounting S3 Buckets in Renku Sessions, which can be symlinked to a convenient location using the script - see this example. This is documented in detail at RenkuLab Docker Images repository.
  • I want to use it for teaching
    Watch Teaching Data Science with Renku and read Teaching with Renku.
  • I am interested in new technologies
    As mentioned previously in the I want to improve the computational reproducibility use case, RenkuLab is built around state-of-the-art tools. 
  • I want to improve the FAIR principles of my digital research assets
    Renku supports the FAIRification and the Renku team is actively developing it further. Read FAIR on Renku (PDF, 28 KB) for a detailed description.

Tutorial - Hands-On [2022-07-05]

After the Introduction, it is a good time to learn more about RenkuLab by doing some practical steps:

  1. Follow the First Steps Tutorial and make sure that you pick the template that corresponds to your interest: R (in RStudio), Python (in JupyterLab), Julia (in JupyterLab) - see the figure below.
    The tutorial will guide you also on how to use the command line interface (the Terminal) in RStudio or JupyterLab.
    Take your time to carefully read the instructions. 
    Make sure that you pick the desired template.
  2. Stop the session corresponding to the tutorial, i.e. "Flights Tutorial" project - see the figure below.
    Stop the session by clicking (1) first on ⋮ and, (2) next on Stop .
  3. Start a new session.
  4. Do some explorative work and save or undo the changes - see here for help.
  5. Stop the session.
  6. Create a new project called "Test_to_delete" using your favourite template.
  7. Delete the "Test_to_delete" project by following the GitLab Docs.

It is a good practice to stop the session once you stop working actively on a project - see more here
As you probably noticed each project has an associated GitLab project on and therefore RenkuLab can provide advanced features: e.g.,

  • collaboration
  • continuous methodologies
  • git LFS (Large File Storage)
  • Docker Container Registry

via GitLab. This is a key feature that we should not underestimate. 

Introduction - Hands-On [2022-07-01]

The Renku project is under active development and its extensive documentation is available at We will use the official documentation to provide you with relevant information.

Please follow these steps:

  1. Watch the Introduction to Renku video and read About Renku.
  2. Watch Why Renku.
  3. Login to and sign in with the SWITCH edu-ID. If you don't have a SWITCH edu-ID, please follow this link.
    Please follow How to Login.
  4. Read What is Renku? . One should distinguish between:
    • RenkuLab, the web platform that we will use during the pilot project, and
    • Renku Client - a command line tool available on RenkuLab that allows you to "manage versioned datasets, automatically create workflows and keep track of computational environments" "is under active development. We encourage you to have a look around, and we are doing our best to keep it stable, but please be aware that changes may be made without prior warning and could lead to service interruptions.

We also kindly request that you do not upload datasets larger than 1GB for the time being. In the future, we will remove this restriction". You can find more about the dataset limitation here.

There are other Renku channels:

Questionnaire [2022-06-27]

We decided to conduct a short questionnaire (≤5 minutes) in order to better know your individual needs and background. This will help us to tailor our information and support during the pilot phase.

To better interpret your answers with respect to your professional role, and to link it to the survey that we plan to conduct at the end of August, we do not record this questionnaire anonymously. 
Note that your data will be treated confidentially (known only to a few people inside Science IT). The conclusions and the anonymously aggregated supporting data from this questionnaire might be presented to relevant stakeholders.

Please fill out the questionnaire (≤5 minutes) at by Monday, the 4th of July 2022, using the UZH email address.

Announcement [2022-06-15]

See the initial Announcement here.

How to Login

The Login is done in 2 steps:

RenkuLab Login
1. Click Login


RenkuLab SWITCH edu-ID
2. Sign in with the SWITCH edu-ID. If you don't have a SWITCH edu-ID, please follow this link .