MJWester
commented
1 year ago Thank you for the comment. We were trying to imply that most of the SMITE implementation is original to the JOSS submission, although some of the algorithms, but generally not the code, have been previously published. In particular, single particle tracking, change detection analysis, dimer hidden Markov modeling and the overall integration into a software package using standard data structures are unique to the JOSS submission. The quoted text has been rewritten as follows:
Some of the algorithms have already been published: 2D Gaussian blob maximum likelihood estimate [@Smith_article:2010], frame connection [@Schodt_article:2021], drift correction [@Wester_article:2021], Bayesian grouping of localizations [@Fazel_article:2022a], diffusion estimation [@Relich_article:2016]. However, this is the first time that they have been integrated together, sharing common data structures. Applications are described in [@FrancoNitta_article:2021; @Mazloom-Farsibaf_article:2021; @Bailey_article:2022]. Typical raw image data can be found in [@Pallikkuth_data:2018]. A summary of the namespaces and classes in SMITE can be found in the online documentation at https://github.com/LidkeLab/smite/blob/main/doc/SMITEclasses.md.
bencardoen
Part of JOSS Review In the paper you write
Please clarify what functionality is unique to the JOSS submission, or if no functionality is unique, clarify that this is a platform/integration of the above tools. You package a lot of features/tools/utilities, so for the reader it can be helpful to have a (tabular?) overview of what functionality is offered.
It's non-trivial for me to find out exactly what is novel between the cited contributions above and the ones documented in the docs
Note that providing an optimized / restructured / re-engineered version of the above is also of value, only at this point it isn't quite clear where the novelty of contribution lies.