Data Usage Template

Data Usage Description Vocabulary: This will describe the use made of one or more data sets. The scope could related data usage with applications, collaborations, data exploration and discovery, trending and metrics, scientific and B2B data handling.

Applications: Where data is used in an application, it will facilitate a description of what the application does and what problem it helps to solve. This will improve discoverability of the application.

• What types of datasets does the application use?
• What types of problems does the application solve?
• What user communities or user-types run the applications?
• What user communities or user-types use the results?
• What types of the applications produce this dataset?

Collaborations: It will describe the role data plays in the collaboration, it will describe how the data is contributed, published, and shared by the collaborators. Collaborative data usage can include data that is governed, handled in projects and studies that have a limited lifespan, and events that are prompted by spontaneous or rare occurrences.

• Governance:

• What is the purpose of the governance?
• What data sets are governed by an organization?
• How do collaborating organizations use the dataset?
• What are the governance policies of the dataset?

• Example: Palo Alto tree data (Tree maintenance, Public Reporting)

• Short Term: (e.g. scientific experiment, school project, census study)

• Electronic laboratory notebook
• Project
• Gaming sites or recreational activities

• Happenings: types of coordinated usage activities that are prompted by a spontaneous or rare occurrence. E.g. Collaborations that occur when meteor showers or aurora borealis sightings are observable to the general public, dependencies are formed between professional societies, scientific observations, and first hand observations experiences shared in twitter.
• Social Networking: usage activities that pertain to media data used in social media (video, pictures, sound, tables etc)

Data exploration and discovery: It will describe the role data plays in data exploration and discovery, it will describe how the data is browsed, navigated, and exploratory experiences shared with others

• How is the data interconnected with other data?
• Are there different vocabularies used to describe the data?
• Sharing “briefing books” that describe discovery process.

Trending and Metrics: It will describe the trends and metrics relating to data usage. This might be include but not limited to:

• How is data advertised?
• How frequently is the data being accessed?
• Is the data up to date or irrelevant?
• What is the data’s perceived intrinsic value?
• E.g.

• Not reproducible/Expensive to reproduce/easily reproducible.
• Not repeatable/Expensive to repeat/easily repeatable.
• Reflects expertise.

• Data consumer stats

• Ratings:

• extremely useful
• easy to use
• useful as a bench mark

Scientific and B2B data handling : It will describe data usage in workflows, pipelines, and situations that rely on complex processing involving the intercommunication between multiple applications, usage of intermediate results, and relationships to provenance.

• What is the business process model or experimental procedure being performed?
• Are there raw or intermediate results that need to be retained?
• What applications are involved in the workflow?