Sharing data brings other benefits as well. A scientific ecosystem in which data is extensively shared and reused would give researchers more freedom to focus on their favorite parts of the discovery process. Data collection requires a different skillset than analysis, especially as the field demands more comprehensive and higher-dimensional datasets, which, in turn, necessitate more advanced analytical methods and software infrastructure. Moreover, while many scientists relish running experiments, others find their passion in analysis. Not every lab has the financial or personnel resources to accomplish this. Accordingly, the tools to study it have become intricate and costly, generating ever-growing torrents of data that need to be ingested, quality-controlled, and curated for subsequent analysis. Why share data? The central nervous system is among the most complex organs under investigation. 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. 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. 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. While embraced in spirit by many, in practice open data sharing remains the exception in contemporary systems neuroscience. 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. 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.
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