Best Practices for Scientific Data Management

Introduction

This guide describes Axiom Data Science’s best practices for scientific data management. The intent of these practices is to improve the accessibility and usability of your data. These practices may be followed at any time during the preparation of your dataset, but are most useful when considered at the onset of project planning and implemented during data collection.

Note

While these practices have been developed with the Research Workspace in mind as the data management platform, the principles involved apply to data management using any software platform.

The guide is divided into two main sections: data management and metadata. The data management section provides general guidance on data management planning, such as how to organize files and folders and the preferred data formats for long-term preservation. The metadata section provides in-depth guidance on how to describe your dataset make it more discoverable.

Follow the links below to nagivate to specific sections of this guide, or use the links in the sidebar to the left. If you can’t find the answer you’re looking for, please contact us at metadata@axiomdatascience.com. Your feedback will help improve this documentation.

Data Management

• Introduction
• Data Management Cheat Sheet
• Data Management Best Practices

Metadata

• Introduction
• Metadata Cheat Sheet
• Metadata Best Practices
• Best Practices for Metadata - Models/Computer Code
• Codelist Definitions

Submitting Data to Axiom

• Introduction
• Data from Sensors
• Data from Models & Grids
• Spatial Data (GIS)
• Biodiversity Data
• Animal Telemetry Data