Bioimage Analysis: A Call for Standards and Community Accountability

Achieving true reproducibility in bioimage analysis is a milestone that may seem far away, but it is a goal to which we must aspire. The current reality is that many published papers lack the necessary documentation, and full disclosure of the steps they followed to obtain results, necessary to reproduce their methodologies accurately. This gap in transparency not only hinders scientific progress but also weakens trust in the findings. It is exactly for this reason that I decided to write this article following a working group session at GloBIAS 2024. During the session, we discussed the urgent need to set clear expectations for data access, bioimage workflow analysis and documentation. By embracing standards such as the FAIR principles, we can change the paradigm and ensure that future research is not only innovative but also accessible, trustworthy, and open.
From my participation, I decided to gather some of the ideas shared and add my perspective.

In bioimage analysis, the reproducibility of workflows is essential to building reliable foundations for research. However, achieving reproducibility remains challenging due to complex data dependencies, varied analytical workflows, and a lack of standardization. What measures should we adopt to address reproducibility challenges in bioimage analysis workflows? Scientific publications can play a crucial role in transforming bioimage analysis into a robust, transparent, and accessible practice. Should the responsibility of ensuring these standards extend beyond researchers to journals and reviewers?

Building Blocks for Reproducibility
In bioimage analysis, as in data management, workflows must follow principles similiar to the FAIR principles:
For a workflow to be truly reproducible, it must first be discoverable. Once workflow is located, it should be easy to access, together with the generated data. Accessibility also depends on creation and adoption of standardized protocols and formats, allowing researchers to access bioimage workflow databases, ideally with an associated forum for questions.

This is particularly important in bioimage analysis, where data from different imaging systems and analysis platforms often need to be combined. By adhering to shared data standards bioimage data can be used in different computational tools, making it easier for researchers to analyze and compare datasets across studies and workflows.
For bioimage workflows to be reused effectively, they must be well-described and documented with detailed, accurate descriptions that reflect which data was used and how it was processed. Rich sample data and documentation allows future researchers to assess a bioworkflow’s relevance to their work and to understand the limitations and strengths of previous analyses. Including comprehensive annotations, details on preprocessing steps, and any transformations applied to the images ensures that subsequent users can evaluate the reproducibility of the results and apply the data in new contexts.

Accountability and Standards: A Community Responsibility?

While individual researchers are responsible for providing fully documented workflows, achieving reproducibility requires a concerted community effort. From my point of view and some of the points that came out in the discussions, here’s how the bioimage community can help:

Setting Benchmarks and Standards

The bioimage analysis community would benefit from a standardized checklist detailing the data and procedural documentation needed for reproducibility. By establishing a clear list of data and metadata requirements, we ensure that researchers and reviewers have consistent guidelines to follow as the Community-developed checklists for publishing images and image analyses. A proposed working group on benchmarking, such as that suggested by Kota Miura from EMBL Heidelberg, could help to continuously update, regulate, and evolve these practices.
Additionally, the community could develop or recommend tools that automate metadata generation, standardize formats, and assist researchers in managing similar to FAIR principles.

Journals’ Role in Promoting Reproducibility

While individual reviewers can evaluate scientific rigor, journals should also play a role in enforcing reproducibility principles by requiring authors to fulfill data and workflow standards. Journals could integrate these checks into their submission processes, accepting submissions only if authors have shared their data, workflow scripts, and relevant documentation. If these standards are unfulfilled, authors should be required to provide additional details or update their methodology accordingly. This step would reduce the burden on reviewers and ensure that only reproducible, well-documented studies are published to support new discoveries.

Peer Review Pre-screening

The responsibility for ensuring a study’s adherence to reproducibility should not fall solely on individual reviewers. Instead, journals could have dedicated reviewers or editorial checks to ensure datasets and workflows meet community standards. This would avoid increasing the workload on general reviewers, allowing them to focus on scientific quality, while ensuring that bioimage workflows are compliant with reproducibility standards.

Community-Driven Recommendations and Resources

As a community, we can create and share resources, such as best-practice guides, and encourage the use of tools like BIAFLOWS. Regular workshops, webinars, and workflow training sessions can keep researchers updated on best practices and emerging tools.

Closing Thoughts
Implementing these practices is essential to building a future where bioimage analysis is accessible, reproducible, and interoperable. As researchers, we not only gain increased trust in our findings but also establish a strong foundation for collaboration and innovation across disciplines. However, this shift requires more than individual efforts—it demands an ecosystem where researchers, journals, reviewers, and institutions all commit to creating and upholding standards.
By investing in this transformation, we create a scientific landscape where high-quality, reproducible bioimage analysis is the norm, not the exception.

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