Psych-DS: A FAIR data standard for behavioral datasets

总结： 行为数据是生物医学研究的核心，包括同步测量（例如大脑激活 和按下功能磁共振成像扫描中的阅读任务的按钮），以及独立执行的那些（例如识字 问卷调查）与神经生理学和大脑成像数据相比，行为数据通常相对较小， 实验脚本和结果数据集的文件大小都是兆字节而不是兆字节。的 行为数据的关键挑战不是大小而是可变性：许多研究不是用FAIR（Findable， 可访问、可互操作和可重用）共享或自动化分析管道。的那些 机器可读的缺乏跨实验室的格式、组织或文档的通用标准， 个别研究。 我们建议在BRAIN倡议中实施人类行为实验标准， 社区开发的数据规范Psych-DS。因为Psych-DS的设计与原始的 数据研究人员已经获得，这项工作将导致广泛的用户采用该标准， 额外的工作或中断现有的工作流程;这些研究人员的反馈将是核心， 制定提案的所有三个目标： 首先，我们将为Psych-DS数据集创建验证器软件，以确保实现标准的工具 对于使用各种语言（Python，R，JavaScript）的研究人员保持交叉兼容， 手工编辑数据集。其次，标准和验证包将被整合到至少三个 心理学家和认知神经科学家广泛使用的流行实验演示包 （jsPsych，PsychoPy和Lookit/Children Helping Science）。第三，支持涉及协调的项目 行为和神经成像/神经生理学数据，我们将实施工具， 脑成像数据标准（BIDS） 关于如何组织行为数据缺乏共识，阻碍了广泛而多样的 生物医学相关研究从与目前正在建设的基础设施完全整合， 支持大规模的神经科学研究。Psych-DS标准的实施和采用将提供 机器可读的数据格式，可用于各种各样的研究背景，并奠定了 为进一步改进生物医学和行为医学领域的标准化和可重复性奠定基础 以理工科为重

Summary: Behavioral data is central to biomedical research, including both synchronous measures (e.g. brain activation and button-presses from a reading task in an fMRI scan), and those performed independently (e.g. a literacy questionnaire.) Compared to neurophysiology and brain imaging data, behavioral data is often relatively small, with file sizes in the megabytes rather than terabytes for both experimental scripts and resulting datasets. The key challenge for behavioral data is not size but variability: many studies are not designed with FAIR (Findable, Accessible, Interoperable, and Reusable) sharing or automated analysis pipelines in mind. Those that are machine-readable lack common standards of format, organization, or documentation across labs or even individual studies. We propose to implement a standard for human behavioral experiments in the BRAIN initiative using a community-developed data specification, Psych-DS. Because Psych-DS is designed to be similar to the raw data researchers already acquire, this work will result in a broad pool of users adopting the standard without additional effort or interruption to existing workflows; feedback from these researchers will be central to development of all three aims of the proposal: First, we will create validator software for Psych-DS datasets, to ensure that tools implementing the standard remain cross-compatible for researchers using a variety of languages (Python, R, Javascript) as well as compiling datasets by hand. Second, the standard and validation packages will be integrated into at least three popular experiment presentation packages in wide use by psychologists and cognitive neuroscientists (jsPsych, PsychoPy, and Lookit/Children Helping Science). Third, to support projects that involve coordinated behavioral and neuroimaging/neurophysiological data, we will implement tools to translate between Psych-DS and the Brain Imaging Data Standard (BIDS). The lack of consensus around how behavioral data are organized prevents a broad and diverse swath of biomedically relevant research from being fully integrated with the infrastructures currently being built to support neuroscientific research at scale. Implementation and adoption of the Psych-DS standard will provide a machine-readable data format that can be used across a wide variety of research contexts, and lay the groundwork for further improvements to standardization and reproducibility in the biomedical and behavioral sciences.