# Sharing Neurophysiology Data from the Allen Brain Observatory: Lessons Learned

arXiv: https://arxiv.org/abs/2212.08638 · HTML: https://mlsanity.com/paper/2212.08638/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2022.

## Built on by (most cited first, 5 of 5)

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- [Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity](https://mlsanity.com/paper/2510.18037/) (2025)
- [Neurodata Without Boredom: Benchmarking Agentic AI for Data Reuse](https://mlsanity.com/paper/2605.12808/) (2026)
- [Leveraging unlabelled data for generalizable neural population decoding](https://mlsanity.com/paper/2607.14086/) (2026)
- [RPNT: Robust Pre-trained Neural Transformer -- A Pathway for Generalized Motor Decoding](https://mlsanity.com/paper/2601.17641/) (2026)

## Abstract (arXiv)

Making all data for any observation or experiment openly available is a defining feature of empirical science (e.g., nullius in verba, the motto of the Royal Society). It enhances transparency, reproducibility, and societal trust. While embraced in spirit by many, in practice open data sharing remains the exception in contemporary systems neuroscience. Here, we take stock of the Allen Brain Observatory, an effort to share data and metadata associated with surveys of neuronal activity in the visual system of laboratory mice. The data from these surveys have been used to produce new discoveries, to validate computational algorithms, and as a benchmark for comparison with other data, resulting in over 100 publications and preprints to date. We distill some of the lessons learned about open surveys and data reuse, including remaining barriers to data sharing and what might be done to address these.