Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine.

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神经科学数据共享的过去、现在和未来：FAIR 实践和基础设施现状的视角

在过去的十年中，神经科学取得了重大进展，从以数据共享贫乏为特征的基本上封闭的科学，转变为公开可用的神经科学数据量急剧增加的基本上开放的科学。虽然这种增长在很大程度上是由大型前瞻性数据共享研究推动的，但我们开始看到神经科学数据长尾共享的增加，这无疑是由期刊要求和资助者授权推动的。伴随着这种向开放的转变，神经科学实践和基础设施对公平数据原则的支持越来越多。FAIR 对于神经科学尤其重要，因为它具有多种数据类型、规模和模型系统以及为其提供服务的基础设施。正如神经信息学早期的设想，神经科学目前由一个全球分布的以神经科学为中心的数据存储库生态系统提供服务，主要围绕数据类型。为了使神经科学数据可查找、可访问、可互操作和可重用，需要不同利益相关者之间的协调，包括产生数据的研究人员、提供数据的数据存储库、跨数据搜索引擎的聚合器和索引器以及提供数据的社区组织。帮助协调工作并制定对 FAIR 至关重要的社区标准。国际神经信息学协调机构领导了神经科学走向 FAIR 的努力，提供了多种资源来帮助研究人员和存储库实现 FAIR。从这个角度来看，我概述了在神经科学领域实现 FAIR 所需的组成部分和实践，并提供了对神经科学 FAIR 基础设施（从实验室到搜索引擎）的过去、现在和未来的思考。