The community-driven initiative Quality Assessment and Reproducibility for Instruments & Images in Light Microscopy (QUAREP-LiMi) wants to improve reproducibility for light microscopy image data through quality control (QC) management of instruments and images. It aims for a common set of QC guidelines for hardware calibration and image acquisition, management and analysis.quality of light microscopy imaging, please sign up at https://quarep.org/contact/.

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While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be properly interpreted (quality), reproduced (reproducibility), and used to extract reliable information and scientific knowledge which can be shared for further analysis (value). In the absence of agreed guidelines, it is inherently difficult for scientists to create comprehensive records of imaging experiments and ensure the quality of resulting image data or for manufacturers to incorporate standardized reporting and performance metrics. To solve this problem, the 4D Nucleome (4DN) Initiative and BioImaging North America (BINA) here propose light Microscopy Metadata specifications that scale with experimental intent and with the complexity of the instrumentation and analytical requirements. They consist of a set of three extensions of the Open Microscopy Environment (OME) Data Model, and because of their tiered nature they clearly specify which provenance and quality control metadata should be recorded for a given experiment. This endeavor is closely aligned with the undertakings of the recently established QUAlity Assessment and REProducibility in Light Microscopy (QUAREP-LiMi; quarep.org) global community initiative. As a result, the ensuing flexible 4DN-BINA-OME (NBO) framework represents a turning point towards increasing data fidelity, improving repeatability and reproducibility, easing future analysis, and facilitating the verifiable comparison of different datasets, experimental setups, and assays. The intention of this proposal is to encourage participation, critiques, and contributions from all imaging community stakeholders, including research and imaging scientists, facility personnel, instrument manufacturers, software developers, standards organizations, scientific publishers, and funders.

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For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.

For the information content of microscopy images to be appropriately interpreted, reproduced, and meet FAIR (Findable Accessible Interoperable and Reusable) principles, they should be accompanied by detailed descriptions of microscope hardware, image acquisition settings, image pixel, and dimensional structure, and instrument performance. Nonetheless, the thorough documentation of imaging experiments is significantly impaired by the lack of community-sanctioned easy-to-use software tools to facilitate the extraction and collection of relevant microscopy metadata. Here we present Micro-Meta App, an intuitive open-source software designed to tackle these issues that was developed in the context of nascent global bioimaging community organizations, including BioImaging North America (BINA) and QUAlity Assessment and REProducibility in Light Microscopy (QUAREP-LiMi), whose goal is to improve reproducibility, data quality, and sharing value for imaging experiments. The App provides a user-friendly interface for building comprehensive descriptions of the conditions utilized to produce individual microscopy datasets as specified by the recently proposed 4DN-BINA-OME tiered-system of Microscopy Metadata model. To achieve this goal the App provides a visual guide for a microscope-user to: 1) interactively build diagrammatic representations of hardware configurations of given microscopes that can be easily reused and shared with colleagues needing to document similar instruments. 2) Automatically extracts relevant metadata from image files and facilitates the collection of missing image acquisition settings and calibration metrics associated with a given experiment. 3) Output all collected Microscopy Metadata to interoperable files that can be used for documenting imaging experiments and shared with the community. In addition to significantly lowering the burden of quality assurance, the visual nature of the Micro-Meta App makes it particularly suited for training users that have limited knowledge of the intricacies of light microscopy experiments. To ensure wide adoption by microscope-users with different needs Micro-Meta App closely interoperates with MethodsJ2 and OMERO.mde, two complementary tools described in parallel manuscripts.

Fluorescence microscopy images should not be treated as perfect representations of biology. Many factors within the biospecimen itself can drastically affect quantitative microscopy data. Whereas some sample-specific considerations, such as photobleaching and autofluorescence, are more commonly discussed, a holistic discussion of sample-related issues (which includes less-routine topics such as quenching, scattering and biological anisotropy) is required to appropriately guide life scientists through the subtleties inherent to bioimaging. Here, we consider how the interplay between light and a sample can cause common experimental pitfalls and unanticipated errors when drawing biological conclusions. Although some of these discrepancies can be minimized or controlled for, others require more pragmatic considerations when interpreting image data. Ultimately, the power lies in the hands of the experimenter. The goal of this Review is therefore to survey how biological samples can skew quantification and interpretation of microscopy data. Furthermore, we offer a perspective on how to manage many of these potential pitfalls.

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