Hi, Rujing-

If you do your processing within AFNI, the history of processing of datasets is stored in the header in what is called the history. So, if you type "3dinfo DATASET" or more specifically "3dinfo -history DATASET". The history accumulates as the processing proceeds, so the n-th step in processing should have n processing commands stored in it. Note that this is just something AFNI does, and using a different software program would lead to the history not appearing at all in the new file.

To estimate the average spatial smoothness in a dataset, you can use 3dFWHMx. Note that this is pretty different than knowing the blur *applied* to the dataset, because the data has inherent spatial correlation/smoothness. Blurring/smoothing during processing adds to this pre-existing smoothness.

Note that estimating the smoothness is again different than asking about what spatial blur was applied because applied blurs are typically specifically Gaussian in form, while the inherent smoothness/spatial autocorrelation within a dataset is typically different. For many, many years, essentially all smoothness-estimation programs in the field use the assumption that the blurriness in the data was approximately Gaussian; Eklund et al. 2016 usefully pointed out that this was not a very good/general approximation. In response to that, AFNI adopted a more generalized autocorrelation function (ACF) form, and this is what 3dFWHMx now uses: "3dFWHMx -acf .." gives you the "mixed ACF" smoothness result, described here:
+ Cox RW, Chen G, Glen DR, Reynolds RC, Taylor PA (2017). fMRI clustering and false-positive rates. Proc Natl Acad Sci USA. 114(17):E3370-E3371. doi:10.1073/pnas.1614961114
https://pubmed.ncbi.nlm.nih.gov/28420798/
+ Cox RW, Chen G, Glen DR, Reynolds RC, Taylor PA (2017). FMRI Clustering in AFNI: False-Positive Rates Redux. Brain Connect 7(3):152-171. doi: 10.1089/brain.2016.0475.
https://pubmed.ncbi.nlm.nih.gov/28398812/
... while "3dFWHMx -classic .." would provide the Gaussian smoothness approximation. Note that the Gaussian smoothness approximation should probably not really ever be used for clustering and things, but for reverse engineering or detective work it might be useful.

I guess in general having a processing script to provide a record of processing is the best way to go, for longer term code remembrance and sharing. Is this dataset something you have already inherited in pre-processed form? Do you have data that you know is "raw", against which you could compare? In many cases, we might suggest processing from the start (e.g., with afni_proc.py if it is FMRI data) rather than having to guess what might have been done.

--pt

ps: and congrats on being the first post of 2023---Happy New Year.