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We believe in data transparency and open access to our protocols and code. We are in process of making our key data resources, code, and protocols available on this page.  Please contact us with any requests for raw data or code to be made publicly available on this site.


Contact:  mjp31 [at]


A Julia implementation of the main fitting routines for reconstruction of Scanning Interference Microscopy (SAIM) datasets and instructions for use are available from our GitHub site: 

Julia package for SAIM analysis >>

Highly optimized fitting routines written in C/C++ are also available bundled with our hardware control package:

C++ code for SAIM analysis >> 

SAIM analysis routines written by Nico Stuurman are also available as a into micro-manager plugin:

Micro-manager package >> 

Nico's publication on SAIM analysis >>


We've recently released v1.0 of an open-source package for autonomous control of laser scanning microscopes.  Drawing inspiration from open-source prototyping systems, like the Arduino microcontroller boards, we devised a hardware-based instrument control platform that was intended to be affordable, easily programmable, and broadly useful for advanced microscopy applications.  The project has been spearheaded by Dr. Marshall Colville and supported through collaborative efforts of the Paszek lab and Zipfel lab

The open-source microcontroller includes precision analog circuitry and optimized firmware routines tailored for diverse laser scanning applications, including azimuthal beam scanning, or “circle-scanning,” which  largely eliminates the uneven excitation field arising from laser interference in through-objective applications, like total internal reflection fluorescence (TIRF) microscopy and Scanning Angle Interference Microscopy (SAIM).  Our complete hardware platform, including hardware design files, firmware, API, and software, is hosted on GitHub with the hopes of continued development through community-based development.

Contact project creator: mjc449 [at]

u-Controller package >>

Related publication >>


The raw scanning electron microscopy (SEM) datasets from Shurer et al. will soon be available.

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