ReviewPatterns (New York, N.Y.)2026
Bridging annotated microscopy imaging data and analysis method development for scientific discovery.
Review in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While modern imaging technologies offer unprecedented opportunities to observe life across scales, distilling an understanding of the underlying biological processes from these complex, high-dimensional data remains challenging. Computational analysis methods have been lagging behind our ability to produce data, as their development often requires expertise across multiple domains, including life and computer sciences. Annotated image datasets play a key role in fostering the development and improvement of microscopy image analysis methods, as they offer a realistic basis to build upon and invaluable ground truth to evaluate and optimize performance. Drawing inspiration from adjacent fields to microscopy imaging, we discuss in this perspective how sharing annotated datasets has driven progress in computational analysis. We emphasize the critical role that open data standards and infrastructure play in realizing the full scientific potential of annotated image datasets and close by highlighting opportunities for members across the scientific community to cultivate a dynamic ecosystem of data, infrastructure, and analysis methods to elevate research quality and accelerate innovation.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.