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README.md

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 # Dataset
 
-Data Repository related to the paper "Cerebellar activity predicts vocalization in fruit bats" (currently under minor revision in Current Biology).
-
-The data is stored in Matlab files containing neural recordings from the cerebellum of fruit-eating bats, captured during the production of both echolocation and social calls.
-
-Data Guide: Cerebellar Activity Predicts Vocalization in Fruit Bats
-
-This repository contains data collected from cerebellar recordings in fruit-eating bats during the production of echolocation and social calls. The data is structured into various Matlab files, with the details of each dataset and its specific parameters explained below.
-
-1. Frequency Tuning LFP 
-This dataset includes Local Field Potentials (LFP) data captured from 224 channels, organized by stimulus level and frequency.
-
--	ft-P2P (224 channels x levels x frequency)
--	besterp: Evoked response data across levels (30:15:90) and frequencies (15:5:85).
--	bestresponse: Best frequency-band level response (BFBL) for each channel.
--	cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT).
-
-2. Frequency Tuning Spikes 
-This dataset contains spike count data from 224 channels, organized similarly by stimulus level and frequency.
--	ft – spike count (224 channels x levels x frequency)
--	N2w: Peri-Stimulus Time Histogram (PSTH) data across levels (30:15:90) and frequencies (15:5:85).
--	bestresponse: Best frequency-Best level response (BFBL) for each channel.
--	cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT).
-
-3. Vocs (Vocalization Data)
-This dataset provides information about the call parameters and classification data for both echo and communication calls.
-
-callparams:
--	dur2: Duration of calls, categorized by call type.
--	pf2: Peak frequency of calls, categorized by call type.
--	finalMatrix: Cross-correlation matrix of different call types.
--	newClass: Identifiers for calls used in cross-correlation.
-
-- SpikeClassifData: Data for classifying spiking responses using Support Vector Machines (SVM).
--	Ne: Normalized echo call spike data for training the SVM.
--	Nc: Normalized communication call spike data for training the SVM.
--	N2: Spike count data.
--	N3: Identifiers for spike count data.
-
-- LFPClassifData: Data for classifying LFP responses using SVM.
--	echoData: LFP data during echo calls for training the SVM.
--	commData: LFP data during communication calls for training the SVM.
-
----
-
-This guide outlines the structure of the data, which is organized into several Matlab files.
+% Data Repository related to the paper "Cerebellar activity predicts vocalization in fruit bats" (currently under minor revision in Current Biology).
+
+% The data is stored in Matlab files containing neural recordings from the cerebellum of fruit-eating bats, captured during the production of both echolocation and social calls.
+
+## Data Guide: Cerebellar Activity Predicts Vocalization in Fruit Bats
+
+% This repository contains data collected from cerebellar recordings in fruit-eating bats during the production of echolocation and social calls. The data is structured into various Matlab files, with the details of each dataset and its specific parameters explained below.
+
+### 1. Frequency Tuning LFP 
+% This dataset includes Local Field Potentials (LFP) data captured from 224 channels, organized by stimulus level and frequency.
+
+% -	ft-P2P (224 channels x levels x frequency)
+% -	besterp: Evoked response data across levels (30:15:90) and frequencies (15:5:85).
+% -	bestresponse: Best frequency-band level response (BFBL) for each channel.
+% -	cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT).
+
+### 2. Frequency Tuning Spikes 
+% This dataset contains spike count data from 224 channels, organized similarly by stimulus level and frequency.
+% -	ft – spike count (224 channels x levels x frequency)
+% -	N2w: Peri-Stimulus Time Histogram (PSTH) data across levels (30:15:90) and frequencies (15:5:85).
+% -	bestresponse: Best frequency-Best level response (BFBL) for each channel.
+% -	cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT).
+
+### 3. Vocs (Vocalization Data)
+% This dataset provides information about the call parameters and classification data for both echo and communication calls.
+
+% callparams:
+% -	dur2: Duration of calls, categorized by call type.
+% -	pf2: Peak frequency of calls, categorized by call type.
+% -	finalMatrix: Cross-correlation matrix of different call types.
+% -	newClass: Identifiers for calls used in cross-correlation.
+
+% - SpikeClassifData: Data for classifying spiking responses using Support Vector Machines (SVM).
+% -	Ne: Normalized echo call spike data for training the SVM.
+% -	Nc: Normalized communication call spike data for training the SVM.
+% -	N2: Spike count data.
+% -	N3: Identifiers for spike count data.
+
+% - LFPClassifData: Data for classifying LFP responses using SVM.
+% -	echoData: LFP data during echo calls for training the SVM.
+% -	commData: LFP data during communication calls for training the SVM.
+ 
+% ---
+ 
+% This guide outlines the structure of the data, which is organized into several Matlab files.