Overview This Zenodo repository contains datasets and code relating to the thesis entitled "Advances in volumetric super-resolution microscopy and single-particle tracking" by Sam G. Daly (Yusuf Hamied Department of Chemistry, University of Cambridge). Managed/updated versions my be avalible at https://github.com/TheLeeLab. The Excel Workbook 'MicrolensRelayCalculator' is designed to help in the design of MLAs for SMLFM. Available Datasets Chapter 4 Simulated localisation data for various PSFs: standard, astigmatism, double helix, SMLFM, and tetrapod; 4000 detected photons, 20 emitters per frame, 200 frames. Microtubule imaging in a fixed HeLa cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames. Chapter 5 B cell receptor imaging on a fixed B cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds. SPT of the B cell receptor on a live B cell (PALM); 20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds. Membrane imaging on a fixed Jurkat T cell embedded in agarose (resPAINT); 20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds. PD-1 imaging on a fixed T cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds. Membrane imaging on a fixed T cell (resPAINT); 20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds. Chapter 6 SPT of ACBD3 in a live HeLa cell (PALM); 20 ms exposure, 640 and 405 nm excitation, 200 frames. SPT of TMD mutant (length: 27) in a live HeLa cell (PALM); 20 ms exposure, 640 and 405 nm excitation, 200 frames. Available Code Autofocus (BeanShell): Counteracts axial drift in SMLFM experiments. Calibration (BeanShell): Controls the piezo scanner for axial calibrations in 3D-SMLM. 3D Reconstruction (Matlab): Reconstructs 2D-localised SMLFM data in 3D. Maintained version available on GitHub. Fiducial correction (Matlab): Removes focal drift artifacts from 3D localisation data. Temporal grouping (Python): Removes multiple single-molecule blinking events. 3D tracking (Matlab): Converts 3D localisations into tracks and calculates diffusion quantities. Matching (Matlab): Determines PPV, sensitivity, and Jaccard index from localisation data. Membrane curvature (Python): Determines the frequency of 3D localisations at a given membrane curvature. Supported by The Royal Society (RGF\EA\181021)