Research data comes in many shapes and sizes and its management changes over time. Kick off your research data journey by exploring different types and forms of research data and how they fit into the research lifecycle.
What research data are we talking about?
- Read an Introduction to Research Data from Boston University
- As we have just seen, research data can come in many forms. Some of these are human readable, and some are machine readable. Explore a couple of these types of formats commonly used for medical, clinical and health data:
Consider: make a list of the forms of data you have used or seen in your work. What would people need to know about these data if they wanted to re-use these data?
Data in the research lifecycle
Data often have a longer lifespan than the research project that creates them. Follow-up projects may analyse or add to the data, and data may be reused by other researchers.
A data lifecycle shows the different phases a dataset goes through as the research project moves from "having a brilliant idea" to "making groundbreaking discoveries" to "telling the world about it"
- Take a look at either:
- Have a look at the NHMRC Statement on Data Sharing (2 pages) and note the lifecycle diagram for data sharing
Consider: have you been through all of the steps outlined in this lifecycle? If not, which ones are new to you?
How data differs across disciplines
1. Choose one of the three specialised data repositories below, or find another data repository of interest - particularly one in a discipline you are unfamiliar with, and spend some time browsing around your chosen repository to get a feel for the data available.
- RCSB Protein Data Bank
- Australian Data Archive (this archive contains Social Science, Historical, Indigenous, Longitudinal, Qualitative, Crime & Justice and International data)
- USGS Water Data
2. Think about how the data here differs from data you are familiar with. Consider for example, format, size and access method.
Consider how cross disciplinary research could be affected by discipline data conventions, and also one way cross disciplinary data access can be facilitated.