Overview

Colandr is a free, web-based platform that supports systematic reviews, from citation import through to full-text screening and data extraction. It’s designed to be collaborative and user-friendly, making it a great tool for teams working across locations.

Website: https://www.colandrcommunity.com

Key Features

  • Web-based, no installation required
  • Supports title/abstract screening and full-text review
  • Allows for data extraction from included studies
  • Designed for collaborative workflows
  • Open-source and actively maintained

When to Use Colandr

Colandr is best suited for full review workflows where you want a simple, all-in-one platform. It works especially well if:

  • You’re working with a team of reviewers
  • You need to track progress and manage tasks
  • You want to keep everything (screening + extraction) in one place

How to Use Colandr

1. Create an Account

Visit https://www.colandrcommunity.com and sign up for a free account.

2. Create a New Review

Set up your project with:

  • Title and description
  • Research question or topic
  • Key review settings

3. Define Review Protocol

Under the “Planning” tab, outline your:

  • Inclusion/exclusion criteria
  • Keywords and search strategies
  • Review objectives

4. Upload References

Supported formats:

  • .RIS or .CSV (from databases like PubMed, Scopus, etc.)

You can upload multiple batches of references.

5. Title and Abstract Screening

Each reviewer marks references as Include or Exclude.

  • Optional: set screening thresholds (e.g., include if 2 of 3 reviewers agree)
  • Progress is tracked per reviewer

6. Full-Text Screening

Upload and screen full-text PDFs, with options to:

  • Add notes or tags
  • Include/exclude with rationale

7. Data Extraction

Use customizable fields to extract:

  • Study design
  • Sample size
  • Outcomes
  • Other relevant variables

All extracted data can be exported for analysis.

Exporting Results

You can export:

  • Screening decisions
  • Extracted data
  • Full dataset of included studies

Formats: .CSV (for meta-analysis or reporting)

Strengths & Limitations

Strengths Limitations
All-in-one workflow Slightly slower on large datasets
Supports data extraction No predictive/prioritization model
Team-based collaboration No API or command-line use
Easy to learn and use Occasionally buggy on PDF uploads