- Research at Western
- Research Theme Program
- For Researchers
- Business Services
- Research Ethics and Integrity
- Research Management Solution (RMS)
- Research Infrastructure
- Research Participation Opportunities
- Research Services Update
- Contact us
Data Management and Technology Planning
Western Sydney University is committed to being a national leader in enabling research data management. Safe storage and back up of your research data is vital to ensure its long term access, security and preservation.
A data management plan will inform ethics applications and computing requirements. Consideration of how much and what type of data, who needs access to it, and plans for future reuse by the same team or new researchers, can lay the foundation for efficient data practices through the life of the project. For more information about planning for data reuse, please visit Data reuse and sharing (opens in a new window)
It may also be necessary to include information about data management planning in a funding application (opens in a new window). The Australian National Data Service (ANDS) provides a guide to filling in the data management section in ARC grant applications (opens in a new window)
The Researcher Dashboard enables the creation of shareable Research Data Management Plans which comply with the University's RDM Policy. It provides instructions on how to obtain Western research data storage for the duration of your project. The 'Describe my data' tab enables researchers to describe and send archival (completed) datasets to Library staff for uploading to ResearchDirect and Research Data Australia (opens in a new window)
Once you have a data management plan, see what technology solutions would be of most benefit to your research. The University is developing technology solutions dedicated to researchers. Check out Connect with Services to find out more about tools for analysis, collaboration and computing. If you would like further consultation on your technology options, please contact Dr Jeff Wang (Intersect eResearch Analyst).
Want to know more? Check out the sections below or head to the Library website for further information about data management.
Standard data management language for research grant applications
We have developed a template blurb that can be used when completing research grant applications, with regards to data management and the use of available University storage options. This should be developed with additional details of data management, backup and sharing plans specific to the project and research area.
The template blurb is as follows:
'In line with the University’s Research Data Management Policy, a comprehensive data management plan will be created using the Researcher Dashboard online tool. Working and archival data from this project will be stored
Western Data Storage Options:
- School/Institute specific solution (eg HIEv at HIE)
- AARNet Cloudstor
- Intersect cloud storage (large datasets)
Additional information is available via the Australian National Data Service (opens in a new window)
Why data management is important
Agencies like the Australian Research Council (ARC) and the National Health and Medical Research Council (NHMRC) now require that an open access repository be used to host the data produced by the research it funds, both in terms of publications and related data. Such a commitment to open access is also articulated in the University Research Code of Practice, which is founded on the Australian Code for the Responsible Conduct of Research (opens in a new window). Furthermore, a number of journals are now also seeking formal compliance with open access for the data underpinning the research they publish.
With good data management, a researcher can get maximum value from their data, plan for their data needs, and preserve and cite their data as long as necessary. It is in the interest of Australia, the University, and researchers to gain the maximum benefit from research which is funded through various means. One of the ways to maximise benefit is to maximise reuse of the data where possible. Good data planning will identify data that can be reused and made available, and on what terms. Some data should be restricted for approved use only, but there is a large variety of data which can be made available and queried or analysed in new ways – sometimes in combination with multiple data sets.
- Data is the basis upon which conclusions are drawn and statements are made. It is the givens, the responses, the variables that result from sampling, measuring, testing, and querying.
- Raw data is processed, transcribed, converted, analysed into a more useful form. The resulting subset is also considered data.
- Often researchers use methodologies, tools, instruments, software programs, and software code to process, convert, or analyse data. For research to be repeatable, and for the data to be reused, it should be kept with any software or tools required to read it and understand it, so in this sense it is part of the data.
- A data set can be applicable to one, or more than one research investigation.
- Data will be created by the current research, or made available from an institution, or another researcher.
- Data may be in physical form, such as tissue samples or paper forms, and may be in electronic form, such as scans, digital video, or spreadsheets.
- Planning out what you will do to meet the requirements of the research, such as restricting access, or making data freely available, is an integral part of conducting research.
Research data follows a life cycle, like this one. It is created, processed, analysed, preserved, and made available for reuse where possible.
- Creating Data: designing, planning consent, collection and management, capturing and creating metadata
- Processing Data: entering, transcribing, checking & validating, anonymising and describing
- Analysing Data: interpreting, deriving, producing outputs & publishing, preparing for sharing
- Preserving Data: migrating, backing-up, storing, creating metadata and documentation, archiving
- Access to Data: distributing, sharing, controlling access, promoting
- Re-using Data: for follow-ups, new research, research reviews, scrutinising, teaching & learning
Source: UK Data Archive – Research Data Lifecycle (opens in a new window)
Storage and archiving
There is a difference between working storage and archival storage. Working storage is where you put the data you're working with/on. Archival storage is where you put the data that you've finished working with, at least for that project, paper or publication. The University offers both types of storage.
In accordance with the University's Research Data Management Policy, infrastructure is available to researchers to use as a secure platform to store and share working data. Visit the University's MyIT portal to learn more about setting up a new file share on the University network, or accessing managed storage through our eResearch support partner, Intersect (opens in a new window)
Working storage for University staff is also available from AARNET (opens in a new window), a federally-funded academic and research company. Cloudstor (opens in a new window) offers working storage and file sharing (opens in a new window) capabilities. Cloudstor is just like Dropbox in many ways, but hosted in Australia, and with higher quotas. You may even wish to request a Group Drive (opens in a new window) from Cloudstor, with 1TB of storage.
In accordance with the Open Access to Research Policy, the products and outputs of research conducted at Western Sydney University (including research data) should be appropriately archived and made openly available for re-use and citation wherever possible.
ResearchDirect is the University's Institutional Repository. It includes data that represents the inputs and outputs of research processes, or a reference to where this data is archived. Library staff will assist researchers to create accurate and complete data descriptions, which are then contributed to Research Data Australia (opens in a new window). This increased exposure to Western Sydney University's research output may enable new collaborations and research opportunities.
To support data citation, records are created with a stable URL or DOI (digital object identifier).