---
title: Project and Data Management
description: "**Use Self-Contained Projects**: For each distinct study or dataset, create a self-contained project to keep your data, annotations, and reports coherently organized. This ensures each dataset remains contextually bounded and prevents mixing materials from different studies. **Document Your Research**: Use…"
url: "https://erroranalyzer.com/docs/project-and-data-management/"
updated: "2025-09-10"
category: Best Practices
---

# Project and Data Management

**Use Self-Contained Projects**: For each distinct study or dataset, create a self-contained project to keep your data, annotations, and reports coherently organized. This ensures each dataset remains contextually bounded and prevents mixing materials from different studies.

**Document Your Research**: Use descriptive project names and detailed descriptions to document your research questions, corpus source, and other contextual details. This practice turns your project into a "digital lab notebook" that supports later interpretation and reproducibility.

**Prepare Data Correctly**: Ensure your data is in a CSV file with exactly two columns, labeled precisely `ErrorText` for the original version and `CorrectedText` for the revised version. This structure is required for the application to properly import and align text pairs.

**Backup and Share Your Work**: Regularly export entire projects as `.zip` archives. This practice serves as a secure backup and allows you to share your complete work—including texts, annotations, and tag schemas—with colleagues for collaboration or replication.