Software at LOCI

LOCI creates open source scientific software. While the primary motivation is to facilitate development of computational techniques for imaging and manipulating living specimens, our software efforts are broader in scope, with the goal of benefiting the entire scientific community. LOCI founded and co-develops the ImageJ2, Bio-Formats, SCIFIO and SciJava projects. LOCI also maintains the Fiji distribution of ImageJ.

Open Source in Science

We believe in open software standards, open source licensing and open development processes.

We do science to discover knowledge and to improve the human condition. Computers help by enabling quantitative research:

  • Data provenance - recording what we did and how we did it
  • Knowledge transfer - explaining the research to someone else
  • Reproducibility - verifying or invalidating the research of others

Developing LOCI Software

Aside from merely knowing how to write code, every professional programmer should master five key tools of the trade:

  1. Distributed version control system (DVCS; e.g., Git)
  2. An integrated development environment (IDE; e.g., Eclipse)
  3. Command line tools (e.g., GNU tools, bash, vim)
  4. Build systems and dependency management (e.g., Maven)
  5. Debugging and debuggers

Interfacing From Non-Java Code

Software written in Java is easiest to use with other Java code. However, it is possible to call Java code from a program written in another language. But how to do so depends on your program's needs.

Frequently Asked Questions

Have questions about LOCI software? Please check the F.A.Q. first.


Mailing Lists

There are several mailing lists available for discussing LOCI software. For questions and comments not related to software, use our contact form.


Source Code

If you are interested in the latest source code for LOCI software projects, check out our organization on GitHub!


Sample Data

All sample data sent to LOCI for testing is kept confidential unless explicit permission is given. See more freely available data sets.


Nico Stuurman
Vale Laboratory
Image Cytometry Standard
256 x 256
42,831 bytes
8 bits per sample
Clay Glennon
256 x 256
83,581 bytes
8 bits per sample
3 focal planes
650 x 515
691,397 bytes
8 bits per sample
3 channels