Structuring Research Repositories for Solar Cell Data

Project Organization

In the perovskitas-para-celdas-solares project, which focuses on exploring materials for solar energy efficiency, keeping research data organized is a recurring challenge. As datasets grow in size and complexity, maintaining a clean directory structure is essential for reproducibility and team collaboration.

The Problem

When working with Jupyter notebooks for data analysis, it is easy to accumulate fragmented data files and helper scripts. Over time, this leads to several issues:

1. Difficulty in locating raw versus processed data.
2. Inconsistent naming conventions across experiments.
3. Challenges in porting analysis workflows to new research environments.

Without a clear hierarchy, researchers lose time searching for files rather than focusing on the physical properties of perovskite materials.

The Solution: Standardized Directory Mapping

We recently implemented a revised repository structure to ensure that all analysis code and artifacts are categorized logically. By grouping related files into functional folders, we ensure that scripts can consistently locate data paths relative to the project root.

Here is an example of the recommended directory layout:

/project-root
  /data
    /raw
    /processed
  /notebooks
    /analysis
    /visualization
  /src
    /preprocessing.py
    /models.py

This structure allows for cleaner notebook imports, as shown in the snippet below:

import sys
import os

# Adding src to path to enable clean imports
sys.path.append(os.path.abspath("../src"))

from preprocessing import clean_solar_data

# Standardized path loading
data_path = "../data/raw/cell_efficiency.csv"

Results of Better Organization

By establishing these boundaries, we have streamlined the onboarding process for contributors and reduced the cognitive load required to navigate the repository.

Getting Started

1. Evaluate your current project structure for "hidden" dependencies.
2. Separate your raw data from your analysis logic.
3. Create a template structure that matches your data lifecycle.

Key Insight

Repository structure is a form of documentation. If a newcomer cannot understand the project's data flow by looking at the folder hierarchy, your project structure is likely the bottleneck to your productivity.

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