The application of microscopy in biomedical research has come a long way since Antonie van Leeuwenhoek discovered unicellular organisms. Countless innovations have positioned light microscopy as a cornerstone of modern biology and a method of choice for connecting omics datasets to their biological and clinical correlates. Still, regardless of how convincing published imaging data looks, it does not always convey meaningful information about the conditions in which it was acquired, processed, and analyzed. Adequate record-keeping, reporting, and quality control are therefore essential to ensure experimental rigor and data fidelity, allow experiments to be reproducibly repeated, and promote the proper evaluation, interpretation, comparison, and re-use. To this end, microscopy images should be accompanied by complete descriptions detailing experimental procedures, biological samples, microscope hardware specifications, image acquisition parameters, and image analysis procedures, as well as metrics accounting for instrument performance and calibration. However, universal, community-accepted Microscopy Metadata standards and reporting specifications that would result in Findable Accessible Interoperable and Reproducible (FAIR) microscopy data have not yet been established. To understand this shortcoming and to propose a way forward, here we provide an overview of the nature of microscopy metadata and its importance for fostering data quality, reproducibility, scientific rigor, and sharing value in light microscopy. The proposal for tiered Microscopy Metadata Specifications that extend the OME Data Model put forth by the 4D Nucleome Initiative and by Bioimaging North America [1-3] as well as a suite of three complementary and interoperable tools are being developed to facilitate the process of image data documentation and are presented in related manuscripts [4-6].


翻译:自Antonie van Leeuwenhoek发现非冰原生物以来,在生物医学研究中应用显微镜有了很长的路要走。无数创新将光显显显显微镜作为现代生物学的基石和将显微镜数据集与其生物学和临床关联联系起来的一种选择方法。然而,不管出版的成像数据看起来多么令人信服,它并不总是传达关于它获得、处理和分析的条件的有意义的信息。因此,充分的记录保存、报告和质量控制对于确保实验的完善和数据真实性、允许实验的重复、促进适当的评价、解释、比较和再使用至关重要。为此,显微镜图像应附有完整的描述,详细说明实验程序、生物样品、显微镜硬件规格、图像获取参数和图像分析程序,以及仪器性能和校准的衡量标准。然而,普遍、社区接受的显微镜元数据标准和报告规格将促成可找到的可读性和可读性(FAIR)显性读取的显微镜(FAIR),并促进适当的评价、解释、解释、比较性、解释性、解释性、解释性、解释性、解释性地数据文件的三维现性数据。我们在这里提出一个可变现的精确数据质量的模型的模型的模型,以增进性、预化、预化、预化、预化、预化的、预化、预化、预化的、预化数据,以增进性、预化、预化的、预化、预化、预化的、预化的、预化的、预化、预化、预化的、预化的、预化、预化、预化、预化、预化的、制、预化性、预化、制、制、制性、预化的、制、预化的、预化的、制、制性、预化的、制性、预化的、制性、制性、制性、制性、制性、制、制性、制性、制、制、制、制、制、制、制、制、制、制、制、制、制。。

0
下载
关闭预览

相关内容

专知会员服务
17+阅读 · 2020年9月6日
Linux导论,Introduction to Linux,96页ppt
专知会员服务
75+阅读 · 2020年7月26日
【哈佛大学商学院课程Fall 2019】机器学习可解释性
专知会员服务
98+阅读 · 2019年10月9日
已删除
将门创投
5+阅读 · 2018年6月7日
Arxiv
0+阅读 · 2021年1月29日
VIP会员
相关资讯
已删除
将门创投
5+阅读 · 2018年6月7日
Top
微信扫码咨询专知VIP会员