About Nano-Org

Nano-Org is an open platform for the sharing, discovery, and analysis of single-molecule localisation microscopy (SMLM) datasets. The platform combines a growing community databank of nanoscale imaging data with advanced computational tools for similarity analysis, contrastive learning, and synthetic data generation.

What Nano-Org Provides

Nano-Org combines data management, sharing, and analysis within a single platform. Researchers can:

  • Browse and search publicly available SMLM datasets.
  • Upload and manage their own localisation microscopy data.
  • Maintain private datasets or share them with selected collaborators.
  • Compare nanoscale structures using similarity search tools.
  • Explore relationships between datasets through contrastive learning.
  • Generate synthetic datasets using SynthMLM.
  • Download data and analysis results for downstream research.

Supporting Open and Reproducible Science

Nano-Org is designed to encourage data sharing, reproducibility, and reuse within the localisation microscopy community.

Publicly shared datasets contribute to a growing repository of nanoscale biological information that can support benchmarking studies, machine learning applications, software development, education, and scientific discovery.

Advanced Analysis Tools

Similarity Search

Nano-Org uses quantitative similarity analysis to compare datasets and identify structurally related nanoscale architectures. These analyses help researchers explore relationships between proteins, cell types, treatments, and experimental conditions across independent studies.

Contrastive Learning

Contrastive learning tools enable users to generate embedding spaces from localisation microscopy data. These embeddings provide an alternative view of dataset relationships, helping researchers identify patterns and biological similarities within large collections of datasets.

SynthMLM

SynthMLM enables the generation of realistic synthetic localisation microscopy datasets derived from existing experimental data. Synthetic datasets can be used for benchmarking, teaching, method development, validation, and machine-learning applications.

The Nano-Org Community

Nano-Org grows through contributions from the research community. Every dataset uploaded to the platform increases the value of the resource by improving opportunities for data discovery, comparison, benchmarking, and reuse. Researchers are encouraged to contribute datasets where possible, either as publicly available resources or as private projects shared with collaborators.

Developed at the University of Birmingham

Nano-Org is developed at the University of Birmingham through a collaboration between Professor Dylan Owen's research group and the Research Software Group within Advanced Research Computing.

To learn how to use Nano-Org, explore the guides available through the documentation menu. Information on citing Nano-Org and associated tools is available on the Citations page.