Lattice Light-Sheet Microscopy Datasets and Workflow for Omero

Dataset Overview This dataset provides a structured workflow for Lattice Light-Sheet Microscopy image processing, including raw data acquisition (.czi), summarised data (extract the .zarr compressed file), metadata extraction, and image enhancement techniques such as deskewing and deconvolution that can be found as a script (main.py). The dataset is intended for researchers working with high-resolution microscopy data. Contents Raw Data: Original microscopy images in CZI format It is recommended to store the raw data (e.g., CZI files) as a baseline for reproducibility. If raw data is too large (e.g., 500 GB), consider downsampling it for testing and archival purposes. Metadata: Embedded data extracted from Zeiss software can be found directly after processing .czi file, while external metadata is synthetically generated (https://github.com/onionsp/Synthetic-WGS-Dataset-Generator/). Processing Scripts: Python scripts (as found in main.py) for deskewing, deconvolution, and data summarization. Use the provided processing scripts to perform deskewing, deconvolution, and other preprocessing steps. Note that processed data can become significantly larger (e.g., a 500 GB raw dataset may expand to 700 GB after processing). Summarized Data: Processed image outputs in .zarr/.tiff format, reducing storage overhead while maintaining key insights. Save summarized data to reduce storage requirements. Summarized data could include key metrics, visualizations, or compressed outputs. Data Transfer Agreement: Documentation regarding data sharing policies and agreements. Workflow Overview Deskewing: Corrects image distortions caused during acquisition. Deconvolution: Enhances image clarity and sharpness. Downsampling: Reduces resolution for efficient processing and sharing. Conversion: CZI to Zarr or TIFF format for optimized storage and computational use. Data Access & Usage The dataset, including raw and processed files, is hosted on Zenodo. Users are encouraged to download downsampled versions for testing before using full-resolution data. Processing scripts enable reproducibility and customization for different research applications. Data transfer policies are outlined in the included Data Transfer Agreement. https://github.com/DBK333/Omero-DataPortal/tree/main/OmeroImageSamples https://github.com/BioimageAnalysisCoreWEHI/napari_lattice数据集概览 本数据集为晶格光片显微镜（Lattice Light-Sheet Microscopy）图像处理提供标准化工作流，涵盖原始数据采集（.czi格式）、汇总数据（提取.zarr压缩文件）、元数据提取，以及去歪斜（deskewing）、反卷积（deconvolution）等图像增强技术，相关实现可于main.py脚本中查看。本数据集面向从事高分辨率显微成像数据研究的科研人员。 内容 原始数据：CZI格式的原始显微图像 建议将原始数据（如CZI文件）作为可复现性研究的基准进行存储。若原始数据体量过大（如500GB），可考虑对其进行降采样以用于测试与归档。 元数据：从蔡司（Zeiss）软件中提取的内嵌数据可在完成.czi文件处理后直接获取，而外部元数据则通过合成生成（https://github.com/onionsp/Synthetic-WGS-Dataset-Generator/）。 处理脚本：包含于main.py中的Python脚本，可实现去歪斜、反卷积与数据汇总功能。请使用提供的处理脚本完成去歪斜、反卷积及其他预处理步骤。请注意，处理后的数据体量可能会显著增加——例如500GB的原始数据集经处理后可能扩容至700GB。 汇总数据：采用.zarr/.tiff格式存储的处理后图像输出，可在保留核心信息的同时降低存储开销。建议存储汇总数据以缩减存储需求，其可包含关键指标、可视化结果或压缩输出文件。 数据传输协议：包含数据共享政策与相关协议的文档。 工作流概览 去歪斜（deskewing）：校正成像过程中产生的图像畸变。 反卷积（deconvolution）：提升图像的清晰度与锐度。 降采样：降低图像分辨率，以提升处理效率并便于共享。 格式转换：将CZI格式转换为Zarr或TIFF格式，以优化存储与计算效率。 数据获取与使用 本数据集（包含原始与处理后文件）托管于Zenodo平台。 建议用户在使用全分辨率数据前，先下载降采样版本进行测试。 处理脚本可支持不同研究应用的可复现性与个性化定制。 数据传输政策详见随附的数据传输协议文档。 https://github.com/DBK333/Omero-DataPortal/tree/main/OmeroImageSamples https://github.com/BioimageAnalysisCoreWEHI/napari_lattice