Map Manager: Longitudinal image analysis with online editing and sharing.

共焦显微镜、双光子显微镜和光片显微镜的可用性和易用性不断提高 荧光蛋白记者的快速发展使3D和功能成像及其分析成为一种 现代神经科学研究的中心组成部分。然而，获取3D和功能图像的简单性是 创建越来越大的数据集，促使对高通量图像分析算法和 既快速又准确的软件。尽管分析单个时间点图像的软件已经收到 尽管如此，目前还缺乏分析多个时间点纵向成像数据集的工具。这 缺乏纵向图像分析工具是科学研究的一个主要障碍，因为各个实验室设计了他们的 自己的分析策略造成了其他人很难核实和复制这种分析的情况。 现在需要的是一个社区同意的纵向图像分析标准，以促进共享。 在这里，我们建议开发软件来创建和管理纵向成像数据集中的注释。 该软件将通过提供所需的严谨性和可重复性来解决主要问题，同时使其变得容易 以便研究人员分发他们的数据和分析。使这些重要的数据集可查找、可访问、 可互操作，可重复使用。为了实现这些目标，我们建议构建直观的Web浏览器和桌面 图形用户界面(GUI)，用于处理基于云的数据和分析。这些图形用户界面将被驱动 通过可编写脚本的Python高级编程接口(API)。对于在线编辑和共享，我们将 与Brain Funded Brain Image Library(BIL)合作，实现与Neurodata的互操作性 边界(NWB)和神经科学数据接口。我们将利用大脑倡议神经形态组织 并定义我们的研究方法(Dory)，以确保我们对形态、连接性、 生理学特征包括可接受的元数据命名法和词汇。 我们将与一组“种子”大脑资助的实验室密切合作，以获取反馈并快速做出 改进了前端图形用户界面和后端API的功能和可用性。这将是 通过在线论坛、网站访问和加州大学戴维斯分校主办的黑客马拉松活动实现。在Covid大流行期间，我们 我了解到，这些活动在虚拟环境下工作得非常好，并准备继续这样做 模特。我们致力于为Web浏览器、桌面图形用户界面和 PythonAPI以及不断改进和简单地遵循基于Web的交互式用例的食谱。至 确保社区采用和使用，该建议还包括与多个“种子”实验室合作来运行 他们的数据贯穿了从分析到在线共享的整个管道。 长期目标是让Map Manager充当数据分析、探索和共享的催化剂。 有效地创建基于社区的方法，类似于天文学等其他学科，其中数据 广泛和公开的共享，允许有效的数据挖掘和模型构建，以推进新的发现。

The increasing availability and ease of use of confocal, two-photon, and light-sheet microscopes coupled with rapid developments in fluorescent protein reporters have made 3D and functional imaging and its analysis a central component of modern Neuroscience research. Yet, the ease of acquiring 3D and functional images is creating progressively larger datasets, prompting the need for high-throughput image analysis algorithms and software that can be both rapid and accurate. Although software to analyze single time-point images has received substantial attention, tools to analyze multiple time-point longitudinal imaging datasets is currently lacking. This lack of longitudinal image analysis tools is a major barrier to scientific inquiry with individual labs devising their own analysis strategies creating a situation where it is difficult for others to verify and reproduce this analysis. What is needed is a community agreed upon longitudinal image analysis standard that promotes sharing. Here, we propose to develop software to create and curate annotations in longitudinal imaging datasets. This software will solve a major problem by providing the needed rigor and reproducibility while making it easy for researchers to distribute their data and analysis. Making these important datasets findable, accessible, interoperable, and reusable. To achieve these goals, we propose to build intuitive web-browser and desktop graphical-user-interfaces (GUIs) that will work with cloud based data and analysis. These GUIs will be driven by a Python advanced-programming-interface (API) that is scriptable. For online editing and sharing we will work with the BRAIN funded Brain Image Library (BIL), and for interoperability with Neurodata Without Borders (NWB) and Neuroscience Data Interface. We will utilize the BRAIN Initiative NeuroMorpho.Org and Defining Our Research Methodology (DORY), to ensure our annotations of morphology, connectivity, and physiological signatures include accepted meta-data nomenclatures and vocabularies. We will work closely with a group of "seed" BRAIN funded labs to obtain feedback and make rapid improvements in the functionality and usability of the front-end GUIs and the back-end API. This will be achieved by online forums, site visits, and a hack-a-thon hosted at UC Davis. During the Covid pandemic we have learned that these events work extremely well when done virtually and are prepared to continue this model. We are committed to providing thorough documentation for the web-browser, desktop GUIs, and Python API as well as constantly refined and simple to follow recipes with interactive web-based use cases. To ensure community adoption and use, this proposal also includes working with a number of "seed" labs to run their data through the entire pipeline from analysis to online sharing. The long range goal is to have Map Manager act as a catalyst for data analysis, exploration, and sharing. Effectively creating a community based approach, akin to other disciplines such as astronomy, where data is widely and publicly shared allowing effective data mining and model building to advance new discoveries.