"Research cannot flourish if data are not preserved and made accessible. All concerned must act accordingly” (Nature, 2009).
This module explores the strategies and issues that need to be considered in appropriately managing data through your research candidature. The information that follows will guide you in managing your data effectively across the research lifecycle. By the end of this module you will:
- apply data management strategies to organise data proficiently, ethically and legally
- get started with creating your data management plan.
Good data management is the basis of successful research and it is important to plan how you will manage your data at the beginning of your project. Good data management practices ensure compliance with the Australian Code for the Responsible Conduct of Research, legal requirements and relevant policies, facilitate data reuse (for yourself and others), and are insurance against catastrophic loss of your raw data.
Research data is information another researcher might need to validate or replicate your research. Data might be facts, observations, images, computer program results, recordings, measurements or experiences upon which an argument, theory, test or hypotheses might be founded. Data can be numerical, descriptive, visual or tactile; it may be raw, cleaned or processed and may be held in any format or media. Research data is not journal articles, conference papers, books or other forms of published results unless this is the topic of your research.
Disciplines may have their own discipline-specific language to describe and interact with research data. This module is relevant both to data created in a digital form ('born digital') or data converted to a digital form (digitised), such as:
- documents (text, Word), spreadsheets
- laboratory notebooks, field notebooks, diaries
- questionnaires, transcripts, codebooks
- audiotapes, videotapes, photographs, films
- test responses
- slides, artefacts, specimens, samples
- collection of digital objects acquired and generated during the process of research
- data files
- database contents (video, audio, text, images)
- models, algorithms, scripts
- contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
- methodologies and workflows
- standard operating procedures and protocols.