Improving the efficiency of your research

Good research data management practices ensure that researchers and institutions are able to meet their obligations to funders, improve the efficiency of research, and ensure data is findable, accessible, interoperable, trusted, and reusable. Research data management is a joint responsibility between researchers and institutions. That is why it’s important to understand what institutions need to consider when formulating data management frameworks and strategies.

You can read more about the importance of data management or find out how researchers can manage their data in the most effective way possible below.

What to consider when using data management frameworks and strategies

Metadata

Metadata are used to facilitate and support resource discovery, identification, the organisation of resources, and the interoperability of the resource(s) it represents, as well as the interoperability of the metadata itself.

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Data governance

Research data governance defines who can use what actions, on what data, in what situations, and using which methods.

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FAIR data

The FAIR principles are designed to support knowledge discovery and innovation both by humans and machines.

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Data Citation and identifiers

Assigning identifiers to each item of data, software or research resource is essential to the future of research.

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Training

Working with data doesn't have to be complex. This is why we're continuously adding to our repository of resources and guides, as well as providing events around skills and training to ensure you're equipped with the best services and tools to manage data.

FAIR data training | 10 and 23 (research data) Things | Training resources

Sensitive data

Confidential and other sensitive information which contributes to or arises from research can include identifiable personal and health/medical data, indigenous data, ecological data about vulnerable species, and commercial-in-confidence data.

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Institutional data management frameworks

These frameworks outline the basic elements required within an institutional context to support effective data management: institutional policy and procedures, training and support services, data infrastructure, data and metadata management, data curation and archival services. ARDC provides workshops to self assess your institution’s current framework and its maturity.

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Institutional policies and procedures

Fundamental to good research data management, policies and procedures are required to address the ownership of research materials and data, their storage, their retention beyond the end of the project, and appropriate access to them by the research community.

 

Funders guidelines

Australian funding agencies have guidelines and requirements relating to research data management that address planning, dissemination and sharing, accessibility and reuse and storage.

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Data management plans

A Data Management Plan (DMP) typically outlines what research data will be created during the course of a research project, plans for sharing and preserving the data, and any restrictions that may need to be applied.

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Data capture

Data with good metadata attached at the point of capture can expedite data sharing, publishing and citation.

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File wrangling

Careful thought about files at the beginning of a research project can save a lot of time, resources and money. Our handy guides delve into best practice around file formats and file naming conventions, making sure you get it right from the start.

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Data versioning

A new version of a dataset may be created when an existing dataset is reprocessed, corrected or appended with additional data. Versioning is one means by which to track changes associated with ‘dynamic’ data that is not static over time.

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Data reuse, licensing and copyright

There are many reasons for sharing and enabling reuse of data, including promoting innovation and potential new data uses. How do you find and access data to reuse, and what can you do with it once you’ve got it?

Licensing and copyright

Linking data with Scholix

Data with good metadata attached at the point of capture can expedite data sharing, publishing and citation.

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Data and journals

Exploring policies around the sharing or publication of data underlying journal manuscripts.

RDA IG | Journal research data policies

Data provenance

The process of documenting where a piece of data comes from and methodology by which it is produced. Answering the questions why and how the data was produced, where and when and by whom.

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