Translation and Clinical Implementation of a Test of Language and Short-term Memory (STM) in Aphasia: The CORE-APHASIA Collaboratory: Advancing Robust Data Science & Sharing (CARDS)

项目总结 失语症是一种语言障碍，影响言语的产生或理解以及 由于中风、头部创伤或其他神经性疾病导致左半球受损而导致的读或写 条件。失语症影响口语和/或书面语处理的任何方面(例如，理解、单词 恢复)，并可从轻微到严重。失语症患者的语言障碍通常具有严重的不良影响 对个人生活质量的影响。现有对失语症的研究努力导致了一系列措施， 干预措施和由此产生的数据集，产生更准确的诊断和精准的针对性治疗计划。一个 这种在诊断精确度和治疗特异性方面亟需的进步的后果是 动力不足的单个病例和病例系列研究的优势，衡量或 跨组的处理内容，以及缺乏标准化和互操作性的多个数据集 开放获取和长期可持续性机制。因此，迫切需要一种技术 基础设施，支持数据标准化和聚合，以及失语症研究人员之间的合作。 我们已经开发了核心失语症、生物医学数据库、知识库和使用 一个由NIH数据科学战略办公室(ODSS)颁发的开放科学平台。核心型失语症 依赖于基于云的开放科学资源，并及时与家长资助的目标相辅相成 它支持开发失语症的临床诊断工具，Temple语言和语言评估 失语症的短期记忆(TALSA)。这一拟议的项目与NIH的数据战略计划保持一致 科学，旨在进一步推进失语症研究人员的数据生态系统，重点确保公平- NESS(可查找、可访问、可互操作和可重复使用)和可信性(透明度、 责任、以用户为中心、可持续发展和技术)。对于这个拟议的项目，我们的 主要的研究目标是将公平和信任的原则和程序纳入我们的退出 核心-失语症生物医学数据储存库和“合作实验室”。作为这项努力的一部分，我们将评估和 增加我们的核心失语症数据资源的比对，以优化其对失语症研究的影响 社区。我们的具体目标是1)实现数据存储库的理想特性，包括 标准化的数据词汇表；2)使用3个方面建立核心-TALSA数据存储库的公平性 标准方法和工具的公正化；3)建立可信度。我们的最终交付成果将是 改进了更成熟的数据存储库，该存储库具有用于增强数据的指标和流程系统 共享、访问和互操作性；提高数据资源的使用率、效用、可持续性和影响；以及 提高根据公正性捕获、整理、验证、存储和分析临床数据的能力 以及值得信赖的能力。

PROJECT SUMMARY Aphasia is an impairment of language, affecting the production or comprehension of speech and the ability to read or write resulting from damage to the left hemisphere from stroke, head trauma or other neurological conditions. Aphasia affects any aspect of spoken and/or written language processing (e.g., comprehension, word retrieval) and can range from mild to severe. Language impairments in aphasia often have profound adverse effects on the individual's quality of life. Existing research efforts in aphasia have led to a wide range of measures, interventions and resultant datasets that yield more precise diagnoses and finely targeted treatment plans. A consequence of this much needed gain in diagnostic precision and treatment specificity has been a preponderance of underpowered single case and case series studies with little consistency of measurements or treatment content across groups, and multiple datasets that lack standardization and interoperability with limited mechanisms for open access and long-term sustainability. Thus, there is a critical need, for a technological infrastructure to support data standardization and aggregation, and collaboration amongst aphasia researchers. We have developed CORE-APHASIA, a biomedical database, knowledge repository, and “collaboratory” using an open-science platform with an award from the NIH Office of Data Science Strategy (ODSS). CORE-APHASIA relies on cloud-based open science resource and is timely and complementary to the goals of the parent grant that supports development of a clinical diagnostic tool for aphasia, the Temple Assessment of Language and Short-term memory in Aphasia (TALSA). This proposed project is aligned with NIH's strategic plan for data science, and aims to further advance the data ecosystem for aphasia researchers with a focus on ensuring FAIR- ness (Findable, Accessible, Interoperable, and Reusable) and TRUST-worthiness (Transparency, Responsibility, User Focus, Sustainability, and Technology) of CORE-APHASIA. For this proposed project, our primary research objective is to incorporate the FAIR and TRUST principles and processes within our exiting CORE-APHASIA biomedical data repository and “Collaboratory”. As part of this effort, we will assess and increase the alignment of our CORE-APHASIA data resource to optimize it's impact on the aphasia research community. Our specific aims are 1) achievement of desirable characteristics for data repositories including standardized data vocabularies; 2) establish FAIR-ness of the CORE-TALSA data repository using the 3 prong FAIR-ify standard approach and tools; and 3) establish TRUST-worthiness. Our final deliverable will be an improved more mature data repository that has a system of metrics and processes in place to enhance data sharing, access, and interoperability; increase usage, utility, sustainability and impact of the data resource, and to improve the ability to capture, curate, validate, store, and analyze clinical data in accordance with FAIR-ness and TRUST-worthiness.