Better reporting is better science: Community-defined minimal reporting requirements for light microscopy

Microscopy is central to discovery in the biomedical and life sciences. Continuously evolving to meet researchers’ needs, it enables scientists to visualize and quantify the invisible and explore questions once thought beyond reach. Advances such as super-resolution imaging, spatial-omics, tissue clearing, and volumetric imaging, to cite just a few, continue to expand our ability to uncover the complexity of biological systems in unprecedented detail. To ensure these powerful techniques effectively drive discovery, detailed and transparent reporting of microscopy methods, including hardware specifications and other key metadata, is essential. Yet, despite imaging’s central role in most biomedical research, methodological details are still too often underreported (Marqués et al., 2020). Improving rigor and reproducibility in microscopy reporting is not merely a matter of compliance; it is an opportunity to strengthen the reliability and impact of our science: improving reporting accelerates discovery, enables others to build upon existing work, and reinforces confidence in published results.

The rapid innovation in advanced microscopy techniques makes it difficult for researchers to stay current or develop deep expertise in every technology. Extensive training requirements often conflict with the pressure for rapid publication, leading to an incomplete understanding of how microscope configurations affect experimental reproducibility, data reuse, and comparability. Unfortunately, microscopes are often treated as “black boxes,” offering little incentive or guidance for learning essential controls that ensure accurate, artifact-free results. Most critically, the absence of standardized publication guidelines prevents consistent, comprehensive reporting.

Encouragingly, efforts to strengthen both methods reporting and the broader understanding of what impacts reproducibility and interpretability in microscopy are gaining momentum. Recent publications and, notably, a pilot initiative by the Nature Portfolio, introducing updated microscopy reporting guidelines, reflect growing recognition of this need (Light microscopy reporting for reproducibility, 2025). However, while many valuable tools and frameworks have been created and made available (Larsen et al., 2023; Hammer et al., 2021; Ryan et al., 2021; Kunis et al., 2021; Montero Llopis et al., 2021; Czymmek et al., 2025; Konshin et al., 2025), their limited adoption or lack of universal endorsement has delayed the standardization of microscopy method reporting.

For reporting standards and guidelines to be effective and widely adopted, they must be accessible, understandable, and relevant across scientific disciplines. Most importantly, such standards must be developed through broad expert consensus and remain applicable across diverse microscopy methods and modalities.

Advancing accurate methods reporting, rigor, and reproducibility in light microscopy has been the central mission of Working Group 11 (WG11) of the Quality Assessment and Reproducibility for Instruments and Images in Light Microscopy (QUAREP-LiMi) Consortium. From its inception, QUAREP-LiMi has united a broad international community of imaging scientists, core facility staff experts, principal investigators, and industry partners committed to improving quality control and reproducibility in microscopy (Nelson et al., 2021; Boehm et al., 2021).

QUAREP-LiMi follows a rigorous, transparent consensus process described in detail in the QUAREP-LiMi bylaws (Grunwald et al., 2025): guidelines and protocols developed within each working group undergo continuous discussion, review, and agreement by all active members of the consortium before being approved by its Editorial Board and Steering Committee. Each QUAREP-LiMi publication thus represents a true, community-wide consensus among experts (https://quarep.org/resources/publications/).

Through this collaborative approach, WG11 developed the bare minimal microscopy reporting requirements checklist (Montero Llopis et al., 2025) (Table 1), a universal framework designed to enhance reproducibility and scientific rigor in light microscopy. The checklist is a learning resource for nonspecialists and experts alike, aimed at deepening the understanding of the essential information that should be included in any publication featuring microscopy data. Its design allows it to be applied broadly, independent of specific modalities or techniques.

The checklist organizes essential metadata into two main categories that capture key aspects of fluorescence light microscopy: how the specimen is prepared (specimen setup) and how images are collected (image acquisition, including hardware and acquisition setup) (Fig. 1). Its structure intentionally aligns with existing community efforts, such as the recommended metadata for biological images framework and the QUAREP-LiMi-endorsed light microscopy model (LiMi-model, formerly NBO-Q metadata model) (Hammer et al., 2021; Sarkans et al., 2021), ensuring consistency across ongoing initiatives. Additionally, it expands and complements the recently published QUAREP-LiMi Working Group 12 recommendations for image publication, processing, and analysis of image data (Schmied et al., 2023) (Fig. 1 image data, image processing, and analyzed data categories).

The checklist is guided by three key principles:

• (1)

  Clarity: making microscopy metadata terminology understandable for all users, with representative examples of how to describe each parameter.

• (2)

  Practicality: ensuring that requirements are achievable in everyday research environments by experts and nonexperts alike.

• (3)

  Ease of adoption: enabling implementation across disciplines, journals, and institutions.

The checklist intentionally focuses on the minimum information essential for transparency and reproducibility in all light microscopy experiments. More advanced techniques will require additional metadata beyond what is captured in this checklist. QUAREP-LiMi WG11 is currently developing modality-specific checklists to meet those needs. Nonetheless, this framework represents an important first step toward a culture of open, rigorous, and reproducible microscopy reporting.

To enhance interoperability, the checklist includes a machine-readable column that aligns each metadata field with the LiMi-model, standardizing terminology. This facilitates integration with existing community-driven open microscopy environment (OME) tools and resources (Zulueta-Coarasa et al., 2025; Goldberg et al., 2005; Allan et al., 2012; Moore et al., 2021, 2023), which already support the automatic capture, storage, and exchange of microscopy metadata in standardized formats. Aligning human-readable method descriptions with machine-readable metadata clarifies how the two complement each other and helps researchers meet FAIR principles (Konshin et al., 2025; Wilkinson et al., 2016), making data findable, accessible, interoperable, and reusable.

Together, these efforts promote and support consistency across imaging platforms and enhance the usability and adoption of the checklist within the broader microscopy community. They represent not only a step toward better reporting but a collective move toward a more open, transparent, and collaborative future for image-based science.

For researchers, the bare minimal microscopy reporting requirements checklist provides a simple yet powerful tool to guide both the design and reporting of microscopy experiments. The checklist helps identify essential metadata and informs deliberate decisions about hardware and acquisition settings that may influence the interpretation and conclusion of results. After completion of the experiments, it ensures that all critical parameters are accurately recorded in the methods. Researchers can follow the clear sequence outlined in the checklist when reporting microscopy experiments: describing the sampling and mounting procedure, including the labeling technique; detailing the equipment hardware selection, including modality modules, optics, illumination, wavelength selection, and detection; specifying how images were captured; and listing all parameters needed to address the specific scientific question. By using the checklist throughout the experimental workflow, researchers not only improve the transparency and completeness of microscopy reporting but also foster thoughtful, conscious experimental design, ultimately improving education in microscopy and strengthening the reliability and reproducibility of their findings.

For reviewers, editors, and funding agencies, the checklist offers a clear structure to evaluate methodological rigor and transparency. Because it aligns with the LiMi-model and OME tools, it integrates seamlessly with existing data standards and review workflows. Its concise design encourages adoption by journals and reduces barriers to establishing common publication standards.

The introduction of this checklist is particularly timely. Across the scientific ecosystem, momentum is building to strengthen reproducibility and transparency in microscopy. Funding agencies such as the NIH and NSF in the United States and the European Commission through Horizon Europe’s Open Science policy are prioritizing reproducibility, FAIR data reuse, open data, and metadata standards in research infrastructure programs. In Europe, initiatives such as the EU-funded project Open Science to Increase Reproducibility in Science further highlight this commitment. As mentioned, publishers are also responding to this movement: several Nature Portfolio journals have recently piloted microscopy reporting guidelines developed in collaboration with QUAREP-LiMi WG11. Meanwhile, community, consortia such as BioImaging North America, Global BioImaging, Euro-Bioimaging, and QUAREP-LiMi, continue to advance and promote shared standards, open data practices, and education in quantitative imaging while fostering international collaboration.

At a time when public trust in science faces mounting pressure, initiatives like this provide a constructive and unifying path forward. By making rigorous reporting more accessible, we empower researchers to communicate their work in traceable, reproducible ways. Each step toward openness strengthens the credibility of scientific discovery and fosters a culture of accountability and excellence. Ensuring that image data are truly FAIR expands the impact of research and enables AI-driven discovery (Zulueta-Coarasa et al., 2025). Through collaboration among scientists, imaging specialists, institutions, publishers, and founders, we can build a research ecosystem where every image, dataset, and result contributes to a more trustworthy and inspiring future for science.

The authors apologize that, due to space limitations, it was not possible to cite all relevant literature. A more comprehensive list of references supporting this work is available at the Zenodo repository from QUAREP-LiMi https://doi.org/10.5281/zenodo.18289058.

We gratefully acknowledge our industry partners for their active participation in discussions and their willingness to engage with QUAREP-LiMi to improve quality control and reproducibility in light microscopy. We also thank the entire QUAREP-LiMi community for fostering a stimulating, collaborative environment that enables open exchange and collective efforts to advance image-based science.

R. Nitschke was supported by grants NI 451/10-1 and NI 451/12-1; H. Hartmann was supported by grant HA 7004/3-1 from the German Research Foundation, and R. Nitschke was supported by grant 03TN0047B “FluMiKal” from the German Federal Ministry for Economic Affairs and Climate Action. M. Cammer is partially supported by New York University Cancer Center Support Grant NCI P30CA016087. D.Grunwald is part-supported by funding from the Wellcome Trust (grant 104931/Z/14/Z) and Biotechnology and Biological Sciences Research Council (grant BB/L015129/1). C. Bertocchi is partially supported from National Research and Development Agency FONDECYT Regular 1250073, Núcleo Milenio SELFO NCN2024_068. D.G. is partially supported by National Science Foundation 1917206.

Author contributions: Paula Montero Llopis: conceptualization, data curation, methodology, project administration, supervision, visualization, and writing—original draft, review, and editing. Chloë van Oostende-Triplet: conceptualization, data curation, methodology, project administration, visualization, and writing—review and editing. Nathalie Gaudreault: conceptualization, data curation, methodology, and writing—review and editing. Caterina Strambio De Castillia: conceptualization, data curation, methodology, and writing—review and editing. Julia Fernandez-Rodriguez: conceptualization, data curation, methodology, and writing—review and editing. Gabriel Martins: conceptualization, data curation, methodology, and writing—review and editing. Alison North: conceptualization, data curation, methodology, visualization, and writing—review and editing. Luis Acevedo: conceptualization, methodology, resources, visualization, and writing—review and editing. Sergiy Avilov: conceptualization, methodology, and writing—review and editing. Cristina Bertocchi: conceptualization, data curation, methodology, and writing—review and editing. Ulrike Boehm: conceptualization, methodology, and validation. Lisa Cameron: conceptualization, data curation, methodology, and writing—review and editing. Michael Cammer: conceptualization and writing—review and editing. Aurélie Cleret-Buhot: conceptualization and writing—review and editing. Steffen Dietzel: conceptualization, data curation, methodology, and writing—review and editing. Orestis Faklaris: writing—review and editing. David Gaboriau: conceptualization, data curation, methodology, and writing—review and editing. Thomas Guilbert: validation and writing—review and editing. David Grunwald: conceptualization, data curation, funding acquisition, methodology, and writing—review and editing. Tingting Gu: conceptualization and writing—review and editing. Nadia Halidi: conceptualization, data curation, methodology, and writing—review and editing. Mathias Hammer: conceptualization, data curation, methodology, and writing—review and editing. Hella Hartmann: conceptualization, data curation, funding acquisition, investigation, and methodology. Janosch Heller: conceptualization, data curation, methodology, and writing—review and editing. Helena Jambor: conceptualization, data curation, visualization, and writing—review and editing. Ayse Aslihan Koksoy: conceptualization, data curation, formal analysis, and methodology. Judith Lacoste: conceptualization, data curation, methodology, and writing—review and editing. DeLaine Larsen: conceptualization, methodology, and writing—review and editing. Sylvia Emmanuelle Le Dévédec: conceptualization, data curation, methodology, and writing—review and editing. Penghuan Liu: conceptualization, methodology, and writing—review and editing. Josh Moore: conceptualization, methodology, and writing—review and editing. Glyn Nelson: conceptualization and writing—review and editing. Michael Nelson: conceptualization, formal analysis, and methodology. Nils Norlin: conceptualization, methodology, and writing—review and editing. Adam Parslow: conceptualization, data curation, methodology, and writing—review and editing. Alexander L. Payne-Dwyer: conceptualization, data curation, methodology, and writing—review and editing. John Peterson: conceptualization, data curation, formal analysis, methodology, and writing—review and editing. Santosh Podder: conceptualization, data curation, methodology, and writing—review and editing. Andrea Ravasio: conceptualization, methodology, and writing—review and editing. Eduardo Rosa-Molinar: writing—original draft, review, and editing. Britta Schroth-Diez: conceptualization and writing—review and editing. Olaf Selchow: conceptualization, methodology, and writing—review and editing. Sathya Srinivasan: conceptualization, data curation, formal analysis, methodology, and resources. Douglas Taatjes: conceptualization, methodology, and writing—review and editing. Kirstin Vonderstein: conceptualization, methodology, and writing—review and editing. Christa Walther: conceptualization, data curation, methodology, and writing—review and editing. Roland Nitschke: conceptualization, funding acquisition, project administration, resources, supervision, visualization, and writing—review and editing.