Talks & Events
==============

.. meta::
    :description: DataLab conference presentations and event participation
    :keywords: DataLab, conferences, presentations, talks, events, SciPy, PyData, Open Source Experience

DataLab has been presented at various conferences and events to share its capabilities with the scientific and open-source communities. This section showcases our presence at major conferences.

Conference Presentations
------------------------

Since its public release, DataLab has been introduced at several prestigious conferences in the scientific Python and open-source ecosystems:

.. grid:: 1 1 2 2
    :gutter: 2

    .. grid-item-card:: 🇺🇸 SciPy 2024
        :link: scipy2024
        :link-type: doc
        :class-card: sd-border-2

        **Tacoma, Washington, USA - July 2024**

        DataLab's international debut at the largest scientific Python conference

    .. grid-item-card:: 🇫🇷 PyData Paris 2024
        :link: pydata2024
        :link-type: doc
        :class-card: sd-border-2

        **Paris, France - September 2024**

        In-depth presentation of DataLab's capabilities and use cases

    .. grid-item-card:: 🇫🇷 Open Source Experience 2024
        :link: osxp2024
        :link-type: doc
        :class-card: sd-border-2

        **Paris, France - December 2024**

        Showcasing DataLab in the broader open-source ecosystem

    .. grid-item-card:: 🇫🇷 Open Source Experience 2025
        :link: osxp2025
        :link-type: doc
        :class-card: sd-border-2

        **Paris, France - December 2025**

        Advanced use case: Automatic X-ray scene reconstruction with DataLab

.. toctree::
   :maxdepth: 2
   :hidden:

   scipy2024
   pydata2024
   osxp2024
   osxp2025

Why Present DataLab?
--------------------

Our conference presentations serve multiple purposes:

**Community Engagement**
    Meeting users, gathering feedback, and understanding the needs of the scientific Python community.

**Knowledge Sharing**
    Demonstrating how DataLab can help researchers, engineers, and data scientists in their daily work.

**Collaboration**
    Building connections with other projects in the scientific Python ecosystem and exploring integration opportunities.

**Feedback Loop**
    Learning from the community's questions and suggestions to improve DataLab's features and documentation.
