Sharing neurophysiology data from the Allen Brain Observatory

Abstract

Nullius in verba ('trust no one'), chosen as the motto of the Royal Society in 1660, implies that independently verifiable observations-rather than authoritative claims-are a defining feature of empirical science. As the complexity of modern scientific instrumentation has made exact replications prohibitive, sharing data is now essential for ensuring the trustworthiness of one's findings. 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. 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.

Keywords: data sharing; electrophysiology; mouse; neurophysiology; neuroscience; open science; two photon calcium imaging.

Figure 1.. Overview of Allen Brain Observatory Visual Coding datasets.

(a) Target brain regions and example visual stimuli. (b) Standardized rigs for two-photon calcium imaging (left) and Neuropixels electrophysiology (right). (c) Example ΔF/F traces or spike rasters for 100 simultaneously recorded neurons from each modality. Both are extracted during one presentation of a 30 s natural movie. (d) Dataset size after different stages of analysis.

Figure 2.. Distributing data from Allen Brain Observatory Visual Coding experiments.

Raw data is acquired and processed at the Allen Institute, combined with metadata (including 3-D neuronal coordinates, stimulus information, eye-tracking data, and running speed) and packaged into NWB files. Each such file is intended to be a complete, self-contained data record for one experimental session in one animal. NWB files are uploaded to three different locations in the cloud: The Amazon Web Services (AWS) Registry of Open Data, the Distributed Archives for Neurophysiology Data Integration (DANDI) repository, and the Allen Institute data warehouse (accessed via the AllenSDK, a Python API for searching for relevant sessions and downloading data). Raw data is also uploaded to the AWS Registry of Open Data. End users can either analyze data in the cloud or download data for local analysis.

Figure 3.. Data reuse over time.

Cumulative number of papers or preprints that include novel analysis of Allen Brain Observatory Visual Coding surveys. Triangles indicate the years in which new data was made publicly available. Paper icons indicate the years in which the Allen Institute preprints describing the dataset contents and initial scientific findings were posted.