Psych-DS: A FAIR data standard for behavioral datasets

Psych-DS：行为数据集的公平数据标准

• 批准号: 10645923
• 负责人: Melissa Kline
• 金额: $ 109.38万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10645923
• 关键词: Adopted; Adoption; BRAIN initiative; Behavioral; Behavioral Sciences; Biomedical Research; Brain; Brain imaging; Child; Cognitive; Communities; Computer software; Computers; Consensus; Data; Data Analyses; Data Collection; Data Set; Development; Documentation; FAIR principles; Feedback; Hand; Human; Individual; Infrastructure; Interruption; Language; Link; Measurement; Measures; Metadata; Mind; Names; Outcome; Persons; Psychologist; Public Health; Pythons; Questionnaires; Readability; Reading; Recommendation; Reproducibility; Research; Research Personnel; Science; Scientist; Specific qualifier value; Standardization; System; Translating; Translations; Update; Validation; Work; analysis pipeline; automated analysis; behavioral study; brain research; data format; data standards; design; experimental study; flexibility; functional MRI scan; improved; indexing; literacy; neural; neuroimaging; neurophysiology; open source tool; prevent; scientific organization; terabyte; tool

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.

概括： 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） 以及功能磁共振成像扫描中阅读任务中的按钮按下），以及独立执行的任务（例如识字 问卷。）与神经生理学和脑成像数据相比，行为数据通常相对较小， 实验脚本和生成的数据集的文件大小均以兆字节为单位，而不是太字节。这 行为数据的关键挑战不是大小而是变异性：许多研究并不是按照公平（Findable，Findable， 考虑到可访问、可互操作和可重用）共享或自动分析管道。那些是 机器可读缺乏跨实验室甚至实验室的格式、组织或文档的通用标准 个人研究。 我们建议在 BRAIN 计划中使用以下方法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 的设计与原始数据类似 研究人员已经获得了数据，这项工作将导致广泛的用户采用该标准，而无需 额外的工作或对现有工作流程的中断；这些研究人员的反馈将成为核心 制定该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，以确保工具实现标准 为使用多种语言（Python、R、Javascript）的研究人员保持交叉兼容 手动编译数据集。其次，标准和验证包将被集成到至少三个 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/儿童帮助科学）。三是支持统筹协调的项目 行为和神经影像/神经生理学数据，我们将实施在 Psych-DS 之间进行转换的工具 和脑成像数据标准（BIDS）。 关于如何组织行为数据缺乏共识，阻碍了广泛而多样化的研究 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到 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，可以在各种研究环境中使用，并奠定了 为进一步提高生物医学和行为学的标准化和可重复性奠定基础 科学。