This dataset provides the data presented in our paper "The representation of time windows in primate auditory cortex" [1] Tonotopic information from each individual animal can be used in the parcellation of function-anatomical areas of the auditory cortex. For characterising tonotopy using the BOLD response to spectral frequencies sound stimuli were based on random-phase noise carrier with different pass-bands: 0.125-0.25 kHz, 0.25-0.5 kHz, 0.5-1 kHz, 1-2 kHz, 2-4 kHz, 4-8 kHz, and 8-16 kHz resulting in stimuli that encompassed different spectral ranges. The carriers were amplitude modulated with a sinusoidal envelope of 90% depth at 10 Hz to achieve a robust response in the auditory system. To record data from the auditory system that is devoid of activity due to the high-intensity noise generated by the MRI scanner, a ‘sparse temporal’ design is utilized. With the use of a pseudo-random sequence, each adjacent trial was ensured to have a different spectral frequency sound stimulus. The duration of each sound stimulus was 6 seconds which were presented during the last 6 of the 10 s trial duration. This duration is sufficient for the BOLD response in the macaque auditory cortex to reach a plateau (Baumann et al., 2010). The monkey performed visual fixation on a fixation point presented in the centre of a visual display in front of the animal during the entire time the sound stimulus was presented. This simple task ensured that the levels of attention remained consistent across the entire session. Moreover, it minimized the body movement of the animal by alleviating potential waiting/boredom related stress. The task was to fixate on a target (small red square) positioned at the centre of a screen, when the eye trace entered within a window of fixation (~ five degrees centred on the target) a timer started and the fixation target turned green. A continuous visual fixation (no saccades) of a randomly defined duration of 2-2.5 s was rewarded immediately by the delivery of a juice via a gravity-fed dispenser. Map of preferred response to different frequency bands is known as ‘best-frequency map’. This map was calculated by identifying voxel by voxel, in each animal across all voxels whose sound versus silence contrast was significant (T>3.1, p<0.001 uncorrected for multiple comparisons across the auditory cortex), which of the frequency conditions showed the highest beta i.e. regression coefficient. The resulting map represents the preferred frequency for each voxel. To map the auditory subfields, information from tonotopy fMRI data, macro-anatomical features (cortical folding), anatomical MRI were combined. The ratio (Joly et al., 2014) of T1w and T2w images provided an index that represented average intensities across the cortical thickness. Highest values of T1/T2 ratio indicated grey matter voxels and were used to identify the location of A1 and R fields. The boundary between A1 and R was identified via the frequency reversal occurring between these regions in the best frequency map of the tonotopy experiment since the posterior end of A1 and anterior end of R prefers high frequency while the anterior end of A1 and the posterior end of R i.e. boundary prefers low frequency. To overcome the similarity of frequency preference between core and belt regions and the difficulty in parcellation of medial belt regions, the T1/T2 ratio is utilized to demarcate between core and belt since this ratio is high in the core regions but lower in the belt regions. The exact method and tools used in parcellation are described here. The subject-specific parcellation of the auditory cortical subfields follows the scheme reported in Reveley et al. (2017). The original atlas was used as provided in the registered format with the population MRI primate brain template published in Seidlitz et al. (2018) and available at https://github.com/jms290/NMT. For each monkey, information from the tonotopic mapping from bold-weighted functional MRI data, macro-anatomical features (cortical folding of the lateral sulcus), anatomical MRI were combined. The lateral fissure was used to run a (local) surface-based co-registration from the NMT template to the subject-native space in order to initialize the registration then non-linear registration was further computed with alignment of the antero-posterior border between A1 and R to the first reversal from High-Low-High frequency reversal from the tonotopic mapping (Joly et al., 2014) using 3D Slicer (ITK based registration framework, www.slicer.org). Finally, the final local lateral adjustment of the full parcellation was applied to overlap the x-coordinate of the centre of the core regions (especially A1/R) to the peak location (within the grey matter) of the T1w-bias corrected map (Geyer et al., 2011; Glasser and Van Essen, 2011; Joly et al., 2014). Thus, the following fields were identified in each hemisphere in each monkey viz. A1, AL, CL, CM, CPB, ML, R, RM, RPB, RT, RTL, RTM, RTp, STGr, and Tpt. If you use this data then please cite this paper: [1] Pradeep Dheerendra, Simon Baumann, Olivier Joly, Fabien Balezeau, Christopher I Petkov, Alexander Thiele, Timothy D Griffiths, "The representation of time windows in primate auditory cortex", Cerebral Cortex, 2021