---
schema: 1
type: term
title: _reproducibility_
description: >-
  **Reproducibility** is the ability to repeat or verify a scientific result,
  which in this material is said to depend on publishing the underlying data
  alongside the published conclusions.
canonical: >-
  https://liquidgold.cc/channel/everyinc/explore/term/reproducibility-e21a5b8e05eb
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/reproducibility-e21a5b8e05eb.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \_reproducibility\_

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/reproducibility-e21a5b8e05eb

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**Reproducibility** is the ability to repeat or verify a scientific result, which in this material is said to depend on publishing the underlying data alongside the published conclusions.

In the episode on AI and science, **reproducibility** comes up as part of the argument that scientific publishing is shifting from narrative-first to data-first: reproducibility depends on publishing the underlying data alongside the conclusions, so that others can re-do the analysis rather than take the story on faith [^820f4796-fbb2-5226-8fee-b27de60aecb0@43].The episode's framing is that traditional publishing captures only successful outcomes — as the guest puts it, “the problem has always been we only publish certain things and so we're not getting any information around what's failed or what's been tried.” Failures and tried-but-abandoned approaches never surface, which skews the picture of scientific progress and makes it harder to check results.The proposed fix is *not* to get rid of the story — stories still matter. What changes is the **data's status**: as access to data and compute grows, the underlying data becomes just as important as the narrative itself. Future papers may include a data appendix alongside conclusions, mirroring the transparency of modern machine learning research, so that results can be reproduced and failure cases become visible rather than hidden.- **Reproducibility** depends on publishing data alongside conclusions [^820f4796-fbb2-5226-8fee-b27de60aecb0@43].- Traditional publishing hides failures, which undermines reproducibility and skews progress.- The story still matters, but the **underlying data is just as important** in a data-first model.