Data Quality and Documentation
How do you describe your digital data and document the steps you take to analyze it? Are your files sufficiently organized and have you created the metadata so that others can interpret what you've done? What about yourself, three months from now? In this guide, we share some best practices and strategies to adopt in order to organize and describe your science research data for yourself and others.
Table of Contents
Tools & Training: Data Organization
Recommended Reading: Data Organization
Tools & Training: Data Analysis
Recommended Reading: Data Analysis
Tools & Training: Data Sharing and Archiving
Recommended Reading:Data Sharing and Archiving
Data Quality and Documentation
Tools & Training: Data Quality and Documentation
Recommended Reading: Data Quality and Documentation
Tools & Training: Data Management Planning
Recommended Reading: Data Management Planning
Metadata Standards
An important part of the process is documenting your data (creating metadata) and making sure that your data are as error-free as possible. Here are some tools to help make that process easier.
- DDI - Data Documentation InitiativeA widely-used international standard for describing data from the social, behavioral, and economic sciences.
- Ecological Metadata Language (EML)"Ecological Metadata Language (EML) is a metadata standard developed by the ecology discipline and for the ecology discipline."
- Digital Curation Center's List of Disciplinary Metadata StandardsProvides information about disciplinary metadata standards, including profiles, tools to implement the standards, and examples of data repositories currently using them.
For those disciplines that have not yet settled on a metadata standard, and for those repositories that work with data across disciplines, the General Research Data section links to information about broader metadata standards that have been adapted to suit the needs of research data."
Need Data Help? Cornell Data Services Is Here!
Cornell Data Services is a cross-disciplinary organization that links Cornell University faculty, staff and students with data management services and best practices to meet their research needs. Ways we can help include:
- creation and implementation of data management plans
- understanding funder requirements and data sharing & archiving
- data organization and collaboration tools
Email us for assistance with your data management questions!