RDM Best Practice Starter Toolkit

Data Management Checklist

This checklist is designed to help you begin integrating data management into your research practice. It can serve as a tool to identify gaps in your current approach and to communicate key elements of data management with your research team. Because practices and procedures often differ between projects, it is best to apply this checklist at the individual project level.

Source: Borghi, J., & Van Gulick, A. (2022). Promoting open science through research data management. Harvard Data Science Review, 4(3). https://doi.org/10.1162/99608f92.9497f68e

Best Practices of Documentations in Research Data Management

What it is

Documentation is a collection of files that describe your team, project, workflows, and data. Building thorough documentation during a study is as important as collecting the data itself.

Why it matters

Standardizes procedures and reduces ambiguity
Supports strategic planning and coordination
Secures data and protects confidentiality
Tracks data provenance and decisions
Helps discover errors earlier
Enables reproducibility of methods and results
Ensures others interpret and use data accurately
Improves searchability via metadata

Four levels to cover

Level	Purpose	When
Team-level	Capture team roles, responsibilities, style guides, and governance.	Establish early; update as roles/processes change.
Project-level	Define project scope, workflows, SOPs, and timelines.	Draft at project start; revise each phase or milestone.
Dataset-level	Describe datasets, sources, capture methods, cleaning steps, and sharing plan.	Before cleaning and whenever datasets change.
Variable-level	Define variable names, types, codes, labels, and transformations.	As instruments are finalized and during cleaning.

How to implement effectively

Use templates and consistent fields across projects—don’t reinvent the wheel.
Assign the person who oversees each process to draft the relevant document(s); collaborate as needed.
Review documents with the Data Management Working Group (DMWG) to gather feedback and reach consensus.
Treat documents as living—update when procedures change or new information arrives.
Version your documents so staff always know the current version and what changed.

Creating and maintaining documentation is an investment—budget time and expertise for it in proposals.

Set expectations: document early, update often, and standardize across the team for consistent, reproducible, sharable data management.

Here is a complied list of documentations to support your entire research life cycle:

Source: Lewis, C. (2025). Data Management in Large-Scale Education Research [Free online version]. Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Retrieved from https://datamgmtinedresearch.com