Data And Code For The Paper Titled 'Variance-Calibrated Cross-Individual Bootstrapping for Small-Sample Neuroscience'

This repository contains the MATLAB source code, simulation results, and empirical datasets associated with the manuscript of the same name, submitted to Scientific Reports.

Abstract

Small sample sizes (N≤10) are a pervasive limitation in experimental neuroscience. This project introduces CIB-VC, a novel bootstrapping framework designed for hierarchical datasets (few subjects, many trials). By combining cross-individual trial recombination with a variance calibration step, CIB-VC achieves nominal coverage and higher statistical power than traditional hierarchical methods.

Repository Structure

The repository relies on a flat file structure for the main analysis code and processed data, with a specific subfolder for the raw biological recordings.

1. Main Directory (Root)

Analysis & Result Generation Scripts

generate_simulation_data.m: The core Monte Carlo simulation engine. It generates the comparative performance metrics for CIB-VC vs. Stratified and Hierarchical bootstrapping.

Note: Running this script takes significant computational time.

robustness.m: Generates Robustness/Dot Plot.

efficiency.m: Generates Efficiency Map.

powerVStypeI.m: Generates Power vs. Type-I Error Plot.

Biological Data:

fte.mat: Processed Frequency Tracking Error (FTE) metrics derived from the raw recordings.

all_conditions.mat: Metadata encoding the experimental conditions corresponding to the FTE data.

Pre-computed Simulation Results Outputs of generate_simulation_data.m, provided to allow immediate figure reproduction:

CIB_VC_sim_summary.csv

CIB_VC_power_summary.csv

CIB_VC_type1_summary.csv

2. Raw Data Folder (/raw_fish_data)

This folder contains the original, individual data files for the N=5 Eigenmannia virescens subjects before preprocessing into fte.mat. These are provided for transparency and archival purposes.

amasra.mat

ardahan.mat

erzincan.mat

gaziantep.mat

samsun.mat

Reproducibility Guide
System Requirements

MATLAB: R2021b or later.

Toolboxes: Statistics and Machine Learning Toolbox.

Warning: This process performs extensive Monte Carlo iterations (R=1500) across multiple scenarios and distributions. It will overwrite the existing CSV files upon completion.

Licensing & Attribution
If you use this method or code in your research, please cite the associated Scientific Reports manuscript:

Uyanik, I. (2026). Variance-Calibrated Cross-Individual Bootstrapping for Small-Sample Neuroscience, Scientific Reports.

Contact
Ismail Uyanik Hacettepe University Department of Electrical and Electronics Engineering Ankara, Türkiye