Scientific Transparency

科学透明度：项目摘要 我们在这次竞争性更新中引入了科学透明核心模块(STCM)。本模块将 下一代数据管理框架内先前图像分析核心模块的活动，以 将可复制和可共享的数据分析带给VRC调查人员。STCM支持科学严谨和 为VRC调查人员提供图像分析的重复性，并从 创新的分析方法，到其在代码中的实现，到其在可重复管道中的应用。模块 人员支持创新分析工具的开发。这一支持的核心要素从 图像分析核心模块，在本供资周期内大量使用。我们的目标是 创建自动化、开源的分析算法，以提供可重现的结果。模块人员将 还支持数据管理和分析管道。这个模块的功能是基于商业广告 飞轮平台(http://flywheel.io).参与调查的研究人员获得的脑成像数据已经 STCM存储在这个系统上；STCM将承担在这个平台上放置视网膜成像数据的成本。 模块人员将进一步协助调查人员将分析代码放置在运行的分析“齿轮”中 存储在飞轮中的数据。这些是软件容器，允许开放源代码分析代码 在几乎任何计算机硬件或操作系统上执行，而不依赖于外部。这些 组件创建可在VRC之间共享的文档化、可重现的分析管道 研究人员和更大的视觉科学界。

Scientific Transparency: Project Summary We introduce in this competing renewal the Scientific Transparency Core Module (STCM). This module places the activities of the prior Image Analysis Core Module within a next-generation data curation framework, to bring reproducible and shareable data analysis to VRC investigators. The STCM supports scientific rigor and reproducibility in image analysis for VRC investigators, and provides support from the inception of an innovative analysis approach, to its realization in code, to its application within a reproducible pipeline. Module personnel support the development of innovative analysis tools. This core element of support continues from the Image Analysis Core Module, which has been heavily used during the current funding cycle. We aim to create automated, open-source analysis algorithms that provide reproducible results. Module personnel will also support data curation and analysis pipelines. This module function is based around the commercial Flywheel platform (http://flywheel.io). Brain imaging data obtained by Participating Investigators are already being stored on this system; the STCM will underwrite the costs of placing retinal imaging data on this platform. Module personnel will further assist investigators in placing analysis code within analysis “gears” that operate on data stored in Flywheel. These are software containers that allow open-source analysis code to be executed, without external dependencies, on virtually any computer hardware or operating system. These components create documented, reproducible analysis pipelines that may be shared between VRC investigators and with the larger vision science community.