- Research at Western
- For Researchers
- REDI Business
- Research Ethics and Integrity
- Centralised Research Facilities
- Research Participation Opportunities
- REDI Update
Data Management and Technology Planning
Western Sydney University is committed to being a national leader in enabling research data management. Not sure if your data management is up to scratch? See if you can answer all the questions in the Data Management Checklist (PDF, 117.94 KB) (opens in a new window)
Do you need a data management plan? Get started by populating the easy-to-use Data Management Plan (DOCX, 73.08 KB) (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. If you would like further consultation on your technology options, please contact eResearch
If your data can be reused, make it discoverable by adding it to the Research Data Repository. This will provide a permanent, citable location for your data. A catalogue entry will be added to the searchable Research Data Catalogue which will make it searchable and allow others to find this data and reuse it. This is also a good way for you, as the researcher, to park your data sets in a secure location where you will be able to find it later. To have your data included, please complete the Library Submission form
The information below will outline some basic concepts and the options available from the University related to data management. One way to get started now is to complete the Data Management Checklist (PDF, 117.94 KB) (opens in a new window)
Standard Data Management Language for research grant applications
We have developed a standard blurb that can be used when completing research grant applications, with regards to data management and the use of available University offerings.
The standard blurb is as follows:
'The working data from this project will be stored in the Western Sydney University Research Data Storage system (OwnCloud or R drive) where it will be secure and systematically backed up. Where appropriate, informed ethics consent for data publication and reuse will be sought from research participants. On completion of the project the data will be archived in a separate secure space and the project team will work with the University Library to catalogue and describe the data in the University Research Data Repository. Where possible a distinct identifier will be obtained for the data which will be made open access and available for re-use in accordance with the draft Western Sydney University Open Access Policy which mirrors the Australian Code for the Responsible Conduct of Research (2007). The description and link to the data where possible will then be uploaded to Research Data Australia'.
Additional information is available in the Guide to Filling in the Data Management Section in ARC Grant Applications (PDF, 194 KB) (opens in a new window) produced by 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)
Data management in a nutshell
Once you have created a plan, following the data management plan is data management in action. To get started, complete the Data Management Checklist (PDF, 117.94 KB) (opens in a new window) and then write up your responses using the Data Management Plan (DOCX, 73.08 KB) (opens in a new window) template.
To better understand data management, the following links provide useful context and detail regarding ways to think about your data and how to craft and excellent data management plan.
- UK Data Archive (opens in a new window) – contains helpful detail, including a managing and sharing brochure.
- Australian National Data Service (ANDS) (opens in a new window) – a federally funded partnership aimed to promote good data management and accessibility of data for reuse. Read about their Data Management insights (opens in a new window)
- Australian Partnership for Sustainable Repositories (opens in a new window) – contains a Data Management Manual for download, as well as lots of good information on best practices.
Finding your data
A description of the data (also called metadata) must be included with your data to make it useful and reusable. It will also help you re-create your conclusions. Describing your data is a key component of taking care of your data.
Research Data Catalogue
For others to search for and reuse research data, the description must be added to a searchable location, such as the University's Research Data Catalogue. The Research Data Catalogue uses data descriptions like a label, and includes a reference to the data itself. The label summarises what the data is, where it fits in with the overall data set or corpus, where it's from, who created (and used) it, and what it's being used for.
For more information on the Research Data Catalogue, please contact the Research Services Coordinator (opens in a new window) in the University Library.
Storage and archiving
First and foremost, bear in mind the difference between working storage and archive storage. Working storage is where you put the data and 'stuff' you're working with/on. Archive storage is where you put the data and 'stuff' that you've finished working with, at least for that project, paper or publication. The University offers both.
For working storage, you can take advantage of a University Research Shared Drive. Read the Research Shared Drive FAQ (PDF, 274.53 KB) (opens in a new window) to learn more. To request a drive, please lodge a Research Shared Drive request via the MyIT Portal (opens in a new window) by first logging into the portal with your Staff Number and password and then clicking on Request Something > Email & Documents > File Shares > Create File share.
Working storage is also available from AARNET (opens in a new window), a federally funded academic and research company. They can offer working storage in the form of Cloudstor, and Cloudstor+ (aka Cloudstorplus). Cloudstor+ is just like Dropbox in many ways, but hosted in Australia, and with higher quotas. If you sign up for Cloudstor+, we would be interested in hearing what you like or don't like about it. Email us your feedback at email@example.com
The Research Data Repository is the place for archival storage for electronic or digital research data, with a citable URL (or digital object identifier (DOI)) which will point to a short description of the data and may include a link to the data itself, depending on the rules of use and access. To learn more about the Research Data Repository visit Research Data Repository Project
There are multiple repositories scattered through the country and through the world, which serve as an archival place for research data.
- Pacific and Regional Archive for Digital Sources (opens in a new window)
- Australian Social Science Data Archive (opens in a new window)
- Institute for Quantitative Social Science (Harvard) (opens in a new window)
- Data Hub (opens in a new window)
- Archaeology Data Service (opens in a new window)
- GeoSpatial Data, University of Sydney (opens in a new window)