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

Institutional data management frameworks

These frameworks outline the basic elements required within an institutional context to support effective data management: institutional policy and procedures, IT Infrastructure, support services, and metadata management.

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Institutional policies and procedures

Fundamental to good research data management, policies and procedures are required tho 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.

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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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Publishing data

Research data is increasingly seen as part of the corpus of scholarly publications. Publishing data means that the data is discoverable, adequately documented, publicly available, and citable to virtually everyone.

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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?

Read more | Licensing and copyright

Data in education

Outreach programs designed to encourage student and community contribution, creating new skills and roles to meet the growing demand for a data savvy workforce.

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Data and journals

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

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Data visualisation

Using data visualisations in your collections has the potential to increase the reuse, discovery and connectivity of your research data.

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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 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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Related topics

Citation and identifiers

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FAIR Data

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Sensitive data

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Metadata

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Your data management toolkit

We’ve put together a list of handy guides and tools to help you ensure you’re always up to date on best practices for data management.