Skip to Main Content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.
Variety in the CAM2 Project
Variety in Big Data projects may not seem immediately evident. Data used in big data projects may have different rates, sizes, and frequencies as well as different policies attached to the data.
Policy - Data providers dictate terms for the use of their dataset. Each of the providers has different policies that specify different download rates, different acceptable uses and different technical specifications such as frame rates. The providers may also have different security requirements (who may or may not access the frames), access/sharing requirements(watermarks or restrictions on how the image may be shared or reused), multiple owners or rights holders for the images (which leads to unclear provenance for future reuse) and many levels of quality for the resulting data due to a variety of equipment (cameras, servers, etc.)
Variety in the data impacts coding decisions in multiple ways. This can include:
- Access to the data
- Quality Control
- Analytical Methods
Quick Tutorials on Data Management
Research Data MANTRA Tutorial: Learn the different types of research data collected.
Storage and Security
ResearchData Mantra Tutorial: Backups, security, and preventing data loss
Sharing and Access
DataONE Education Modules: Module 2 Data Sharing - The value of sharing data and concerns about sharing data
Data Protection, Rights and Access
Research Data Mantra Tutorial: Intellectual Property and privacy
Quality Control and Assurance
DataONE Module 5: Best practices for quality assurance
Storage and Security
Research Data Mantra Tutorial on Storage and Security: Options to safely store data
DataONE Module 7: Metadata What is metadata? Why should you use it?
Metadata for Discovery
Introduction to discovery level metadata, talk about key categories of this metadata, and show several examples
Analysis and Workflows
Data One Module 10: Analysis and Workflows Types of analyses and how they influence workflow.