EuroSciPy aims to be the meeting point of maintainers of scientific open source projects, with other contributors and their users.
The maintainers track are informal sessions intended for discussions among contributors.
Please email us if you have a proposal for a maintainers session.
All the sessions will take place in the
- 11:05 am - 11:50 am: Education - Materials, methods, tools - Mx Chiin-Rui Tan
- 1:30 am - 2:15 pm: Interoperability in the DataFrame landscape: DataFrame API & PyArrow Update - Joris Van den Bossche
- 2:35 pm - 3:20 pm: Python in the Browser - Roman Yurchack & Sylvain Corlay
- 10:30 am - 11:15 am : Scientific Python / SPECs - Jarrod Millman
- 11:25 am - 12:10 pm : Contributor Experience & Diversity - Noa Tamir
Education - Materials, methods, tools
Chair: Mx Chiin-Rui Tan (She/Her, They/Them)
This session focuses on issues related to education in the ecosystem, from three different aspects, and during the session we focus on recent advances and existing and upcoming challenges.
- Materials: how are projects dealing with documentation and education materials
- Methods: What should we do to make our materials more accessible to underrepresented and/or historically marginalised groups?
- Tools: What are the existing tools in the ecosystem helping us achieve the above goals, and what do we need to develop?
We will give an overview of these different aspects.
Interoperability in the DataFrame landscape: DataFrame API & PyArrow Update
Chair: Joris Van den Bossche
In this session, we want to share some updates on the DataFrame ecosystem: the DataFrame interchange protocol (https://data-apis.org/dataframe-protocol/latest/purpose_and_scope.html) and Arrow C Data interface (https://arrow.apache.org/docs/format/CDataInterface.html), and the integration of those interoperability protocols with different libraries. Further, we want to have an open conversation about challenges and requirements related to DataFrame interoperability and supporting multiple DataFrame libraries in projects.
Python in the browser
Chair: Roman Yurchak, Sylvain Corlay
Recently it bacame possible to run Python and the scientific Python packages in the browser thanks to WebAssembly and Emscripten. This is done in particular in the Pyodide and emscripten-forge projects. It allows for a scientific Python application, or a compute environement such as JupyterLite, to be seamlessly accessible to large number of users with very little effort or infrastructure requirements.
At the same time, the scientific Python ecosystem did not evolved with the web in mind. We will discuss some of the challenges package maintainers may face when trying to run their package in the browser, and what could be done to overcome these.
Scientific Python / SPECs
Chair: Jarrod Millman
The Scientific Python project aims to better coordinate the ecosystem and grow the community. This session focuses on our efforts to better coordinate project development, and to improve shared infrastructure. In this session together we will discuss project goals and recent technical work.
The Scientific Python project’s vision is to help pave the way towards a unified, expanded scientific Python community. It focuses its efforts along two primary axes: (i) to create a joint community around all scientific projects and (ii) to support maintainers by building cross-cutting technical infrastructure and tools. In this session we mostly focus on the second aspect.
The project has already launched a process whereby projects can, voluntarily, adopt reference guidelines; these are known as SPECs or Scientific Python Ecosystem Coordination documents. SPECs are similar to projects specific guidelines like PEPs, NEPs, SLEPs, and SKIPs, to name a few. The distinction being that SPECs have a broader scope, targeted at all (or most) projects from the scientific Python ecosystem.
The project also provides and maintains tools to help maintainers. This includes a theme for the project websites (currently used on, e.g., numpy.org and scipy.org), a self-hosted privacy-friendly web analytics platform, a community discussions forum, a technical blog, and project development statistics.
We present all these tools, discuss various upcoming SPECs, and highlight the project’s future potential.
The Scientific Python project is already supported by eight core projects: IPython, Matplotlib, NetworkX, NumPy, pandas, scikit-image, scikit-learn, and SciPy. The organization has spent the last several months working on the infrastructure, and is now ready to engage more widely to help grow and support the community.
Contributor Experience & Diversity
Chair: Noa Tamir (She/Her, They/Them)
Most of us have been hearing about Diversity Equity and Inclusion (DEI) for some years now, and even had access to many resources by now. Our projects have codes of conduct, and some have been doing sprints and mentorships. But how much has fundamentally changed?
Let’s meet for an honest conversation about the challenges of DEI actions, and culture change. How do we achieve long-term impact? What are low-hanging fruit? We can share hard-to-ask questions, effective tools, experiences that shaped our approach, and see if we can all nudge each other forward a little.
Inclusion happens at the community level, also when we want to address DEI itself. So, we will need to create a safe space for hard questions and leave judgment at the door.
Thanks to our grant to advance an inclusive culture in the scientific Python ecosystem, we have created the contributor experience lead role. We have been working with NumPy, SciPy, Matplotlib, and pandas to learn how to integrate this new role to a project, and how to introduce contributor hospitality techniques. We are working on creating widely available resources, and we would benefit from hearing from maintainers from the wider community.
There is at least one confirmed maintainer from the following projects at the conference:
- Pandas: Joris Van den Bossche
- Apache Arrow: Joris Van den Bossche
- Pyodide: Roman Yurchak
- scikit-learn: Guillaume Lemaitre, Julien Jerphanion, Adrin Jalali, Jérémie du Boisberranger, Olivier Grisel, Loïc Estève, Gaël Varoquaux, Roman Yurchak
- Project Jupyter: Sylvain Corlay, Jeremy Tuloup
- mamba: Sylvain Corlay, Wolf Vollprecht
- fairlearn: Adrin Jalali
- networkx: Mridul Seth
- bokeh: Mateusz Paprocki
- scikit-image: Emmanuelle Gouillart
- Jax - Flax: Andreas Steiner
- Matplotlib: Tim Hoffmann
- conda: Jannis Leidel, Jaime Rodríguez-Guerra
- conda-forge: Jannis Leidel, Jaime Rodríguez-Guerra
If your organization is covering the expenses of an open source maintainer to attend the conference, let us know and we will list it here.