Good Data Practices

Good data practices mean researchers and institutions can meet obligations to funders, improve the efficiency of research, and ensure data is findable, accessible, interoperable, and reusable (FAIR). Doing data right is a joint responsibility between researchers and institutions.

The ARDC has collated the following best-practice guidance to help you manage your research data in the most effective way possible.

Your Guide to Good Data Practices

FAIR data principles are designed to support knowledge discovery and innovation both by humans and machines. Read more.

Sensitive data can include identifiable personal and health/medical data, indigenous data, ecological data about vulnerable species, and commercial-in-confidence data. Find out more.

Metadata are used to facilitate and support resource discovery, identification, the organisation of resources, and interoperability. Learn more.

Data citation and identifiers are essential to the future of research. Find out more.

Data governance defines who can use what actions, on what data, in what situations, and using which methods. Watch our video.

Data provenance means documenting where a piece of data comes from and methodology by which it is produced. Read more.

Data versioning involves a new version of a dataset being created when an existing dataset is reprocessed, corrected or appended with additional data. It can help track changes associated with ‘dynamic’ data over time. Read more.

Data reuse, licensing and copyright can help maximise your data. Read our guide.

Data management plans (DMPs) typically outline what research data will be created during a project, plans for sharing and preserving the data, and any restrictions that may need to be applied. Read more.

Institutional frameworks should outline the basic elements required to support effective data management. The ARDC provides workshops to self assess your institution’s current framework and its maturity.

Institutional policies and procedures are fundamental to good research data management and address the ownership of research materials and data, their storage, their retention after a project, and access by others later. View our guide.

File wrangling means putting careful thought into files at the beginning of a research project to save yourself time, resources and money down the track. Check out these guides, which include file formats and naming conventions.

Linking data with Scholix, the Scholarly Link Exchange, can improve links between literature and research data. Learn more.

Journals have data policies regarding the sharing or publication of data underlying journal manuscripts. Journal research data policies | Research Data Alliance Interest Group

Training resources and guides, including ARDC courses, can equip you with services and tools to manage data. FAIR data training | 10 and 23 Things Training resources

Last updated

14 May 2022

Type

Page

Research Topic