Demo, limited dataset for reviews in Nature Communications

Francisco Garcia-Rosales b48fc52c42 Update '' 8 months ago
GLM_Model_Outcomes e68a5d5a3d Xóa 'GLM_Model_Outcomes/pvals_GLMM_norm.csv' 8 months ago
bycycle_run_example_output a6e89661aa Upload files to '' 8 months ago
matlab_vars d1997ac15e Upload files to '' 8 months ago
raw_LFP_traces 276bf4e42e Upload files to '' 8 months ago
LICENSE e8eaac87da Initial commit 8 months ago b48fc52c42 Update '' 8 months ago
datacite.yml 2b758ce7be Update 'datacite.yml' 8 months ago

Below follows a description of the provided dataset, and how to use these data with certain scripts. Be aware that this is a demo dataset.

Description of data in folders.

master > raw_LFP_traces:

An example paired penetration containing raw LFP data from two A16 electrodes, recorded simultaneously, from the auditory and frontal cortices, while animals vocalized freely. The names of the files are* ColDataVocs1110.mat*, and ColDataVocs2110.mat. Each represents a 2 different acoustic recordings sessions (in succession) related to the same penetration pair.

These files contained matlab structures with meta-information regarding the recording (i.e. sampling frequency, electrophysiology triggers, number of channels, etc.). Raw LFP traces can be found in the variable col_data.column_data([channel]).raw_LFP. Channels 1:16 are in the frontal cortex; channels 16:32 are in the auditory cortex.

These data can be used to visualize LFPs, or execute an example run of the cycle-by-cycle algorithm, in python.

master > GLM_Model_Outcomes:

Tables (in .csv) containing information about the outcomes of the GLMs ran across channels and frequency bands (these are results from the R scripts). This files are directly used by the script “*pproc_stats_GLMM_fromR.m*”, which visualizes the results of the models.

master > bycycle_run_example_output:

Here are two .csv tables with the detailed output of the cycle-by-cycle algorithm, for one penetration pair (the same one provided with the raw LFPs; note that there are two sessions of acoustic recordings). These outputs are obtained from running the python codes associated with the bycycle analyses.

master > matlab_vars:

Mostly MatLab variables (.mat files) of actual results from the paper. For example, dPTE connectivity matrices obtained from running the script “*core_get_PTE_population.m*” are provided (file: PTE_echo_non_echo_201127_pre_post_500len.mat). These variables can be used with a variety of scripts in order to visualize the results. The variable related the dPTE matrices can be directly used, for instance, to generate connectivity graphs.

Other useful variables that may be needed to appropriately run scripts are also included here. Not all are MatLab variable files: “*Data_Files_Arrangement_A16x2.xlsx*” is a table that contains the organization of the raw data, directly from recordings.


If scripts are to be run, please make sure that all variables are in the local computer and that the local path is added in MatLab. It may be necessary to pre-load a variable file before running a script. Note that scripts usually begin with a “clear all” statement, so loading variables and then blindly running a script is likely to fail. Rather, modify the first sections of the scripts’ code according to whether variables are already loaded, or to the position of these variables in the local computer.

Title Supporting, small dataset for Garcia-Rosales et. al (2021), in review: Nature Communications
Authors Garcia-Rosales,Francisco;Goethe University, Frankfurt;ORCID:0000-0001-5576-2967
Hechavarria,Julio;Goethe University, Frankfurt;ORCID:0000-0001-9277-2339
Description Scripts provided to reviewers and Editors are associated with these data. These codes are available from the authors upon reasonable request.
License Creative Commons CC0 1.0 Public Domain Dedication (
Funding DFG, HE 7478/1‐1
Keywords Neuroscience
Resource Type Dataset