Research Data Management: Create and Acquire

Creating and Acquiring Data

Data is usually created during the observation, computation, or experiment stage. It can also be acquired from a third party.

Acquisition of data involves collecting or adding to data that one currently has. There are different ways of acquiring data: collecting; converting or transforming; sharing or exchanging; and purchasing.

It is best practice for you to develop a well-organized plan to be able to manage that data. You need to give adequate thought as to how the data will be used in the project, and to think about whether or not the data will be made available to others in the future. Such planning will assure appropriate use of the data, and address issues of access, privacy, confidentiality, and security of the data.

Types of Data

As we already know, data covers a lot of different things, and can mean different things to different people. When talking about research data management, the types of data acquired or created can generally be put into four different categories.These are:

Observational Data: 

• Captured in real-time, either through human observation, surveys, or the use of an instrument or sensor
• Usually irreplaceable
• Examples: Sensor readings, telemetry, survey results, images

Experimental Data:

• Data from lab equipment which is produced when a researcher tries to produce and measure change or difference, by changing one or more variables
• Often reproducible, but can be expensive
• Examples: gene sequences, chromatograms, magnetic field readings

Simulation Data:

• Data generated from computer test models, when a researcher tries to predict what might happen under certain conditions
• Models and metadata
• Input more important than output data
• Examples: climate models, economic models, seismic activity

Derived or compiled

• This is data that is produced from different data sources; the data has been somehow transformed to create new data
• Reproducible (but very expensive, and could be time-consuming)
• Examples: text and data mining, compiled database, 3D models

The type of data that is collected can affect the way that the data is managed.