Documenting Research Data
This guide is a collection of resources shared in Data Services' "Documenting Your Research Data" online modules
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Overview of Tabular Documentation
- Cessda Data Management Expert Guide: Using tabular data sets as examples, free training module on how to document data at both the project and data set level.
- Preparing Tabular Data for Description and Archiving: General guidelines for documenting tabular data including formatting and organization. Created by the Cornell Research Data Management Service Group.
- Best Practices for Filenaming, Organizing, and Working with Data: From the Smithsonian Libraries, basic principles for managing tabular data. Includes two pdfs on "Best Practices for File Naming and Organizing" and "Best Practices for Working with Tabular Data".
Types of Documentation
- Codebook Cookbook: Practical guidance on how to create a codebook using Excel examples.
- Data Dictionaries: Created by the USGS, educational overview of data dictionaries including multiple examples.
- README Txt: Blog about the purpose of README files for research data and suggestions on what to include in one.
Tools to Help Automate the Creation of Documentation
- Colectica for Excel Standard Edition: Free tool for documenting your data directly within Excel including variables and code lists.
- Reproducible Research Toolkit: Module 4: Guidance on writing README files and codebooks for data and code. The training also includes descriptions of using various statistical packages, including R, to automatically generate a codebook.
- Getting Started Creating Data Dictionaries: How to Create a Shareable Dataset: Tutorial on creating data dictionaries and codebooks. Includes free apps to assist with documentation creation.
- How to Automatically Document Data With the Codebook Package to Facilitate Data Reuse: This article describes how to use the codebook R package and the author's web app to document your data.