---
title: "Community Insights: Best Practices for Open Datasets for LLM Training"
license: CC-BY-2.0
license_url: http://creativecommons.org/licenses/by/2.0/be/deed.en
license_note: "The FOSDEM video directory https://video.fosdem.org/2025/ub5230/ states: This work is licensed under the Creative Commons Attribution 2.0 Belgium Licence, with the deed at http://creativecommons.org/licenses/by/2.0/be/deed.en. Confirmed 2026-10-18. The VTT file is the publisher caption of that same recording. This note is an original summary, not a transcript."
source: https://archive.fosdem.org/2025/schedule/event/fosdem-2025-6020-community-insights-best-practices-for-open-datasets-for-llm-training/
source_license: "CC BY 2.0 Belgium"
attribution: "Summary by iinventedlight (AI-assisted). Source: Kasia Odrozek, Community Insights: Best Practices for Open Datasets for LLM training, FOSDEM 2025 Mozilla devroom (CC BY 2.0 Belgium)."
date: 2026-10-18
status: published
category: talks
tags: training data, licensing, personal data, assistants
checked_on: 2026-10-18
sources:
  - id: S1
    title: "Community Insights: Best Practices for Open Datasets for LLM training"
    authors: "Kasia Odrozek"
    url: https://archive.fosdem.org/2025/schedule/event/fosdem-2025-6020-community-insights-best-practices-for-open-datasets-for-llm-training/
    accessed: 2026-10-18
    license: CC BY 2.0
    attribution: "Kasia Odrozek, Community Insights: Best Practices for Open Datasets for LLM training, FOSDEM 2025 Mozilla devroom (CC BY 2.0 Belgium)."
  - id: S2
    title: "Publisher captions (VTT) for the same recording"
    authors: "FOSDEM"
    url: https://video.fosdem.org/2025/ub5230/fosdem-2025-6020-community-insights-best-practices-for-open-datasets-for-llm-training.vtt
    accessed: 2026-10-18
    license: CC BY 2.0
    attribution: "FOSDEM, captions for Kasia Odrozek, Community Insights: Best Practices for Open Datasets for LLM training (CC BY 2.0 Belgium). License stated on https://video.fosdem.org/2025/ub5230/."
---
# Community Insights: Best Practices for Open Datasets for LLM Training

License: [CC BY 2.0 Belgium](http://creativecommons.org/licenses/by/2.0/be/deed.en). Source: https://archive.fosdem.org/2025/schedule/event/fosdem-2025-6020-community-insights-best-practices-for-open-datasets-for-llm-training/

## Summary
Kasia Odrozek (Mozilla), speaking at FOSDEM on 2 February 2025, describes why large labs stopped publishing the sets they train on after copyright suits, and what a more open practice would look like. She sorts openness into three levels: documentation enough for someone else to try to repeat the work, a download with no license claim, and a set people may reuse, share, and change, including its pieces. Building that third kind from openly licensed and public-domain material is hard. License statements on crawled pages are incomplete, public-domain copies are often not digitized, PDF text is messy to extract, and a small group may have no legal entity behind the release. A consent fight is also blocking researchers, including through robots.txt. The recommendations she walks through include preference metadata, written documentation, toxicity classifiers, data trusts, opt-out interfaces, a safer harbor for small labs, machine-readable license and consent fields, and public-goods funding. Attribution of the Creative Commons items inside a set is still unsolved; one attempt is a column in the data file. This note follows the talk captions. It does not use the research paper linked from the schedule page.

## Why it is here
The library is asked how to keep personal data safe, and the assistant notes already assume models were trained on somebody's writing. This talk is a public account of what "open" training data does and does not mean, and why consent and license scope still matter after a file is posted.