Research Data Management Planning
At the start of your research project you will be planning to either use data from other sources, or collect your own data.
These are some of the things you should plan for:
- What kind of data will you be collecting? Think about methodologies (and the type of data they are likely to produce). The Sage Research Methods Online database has examples of over 150 datasets, and data analysis techniques for quantitative and qualitative research that might be of interest.
- How will you manage, describe and store the data files? What statistical analysis software and file types and what file naming conventions/folders/metadata schemas you will adopt.
- Will your data contain sensitive information requiring secure storage, and conditions of access as well as the usual Ethics approvals?
- If you decide to share your data, how will you license it to ensure copyright or moral rights protections
- How long your data should be retained, or embargoed
The Data Management Plan
Who should submit a Plan?
Research Data Management (RDM) is a recommendation of the Australian Code for Responsible Conduct of Research as well as being required by many funding bodies. Many funding bodies require RDM to be specifically addressed in the applications.
To ensure CSU researchers follow good RDM practice CSU has established a RDM policy which requires all active research projects (whether funded externally or not) to have a RDM Plan.
The plan can be added to or amended as the research project evolves. If you need assistance contact email@example.com
Approval and peer review process
For HDR students, your supervisor needs to sign the form. The plan will be emailed to you, forward it to your supervisor. The supervisor can sign using the 'signing' feature on the PDF.
Help is available at any stage of research. Ask your experienced colleagues, your supervisor, or contact firstname.lastname@example.org
Research Data Management training is available at: ELMO RDM training
Data management support, consultations and training at CSU
CSUs Research Office Professional Development Calendar - offers training on Research Data Management, Nvivo software training, Data Visualization, and more.
CSU also offers the ELMO course : Introduction to Research Data Management
Quantitative Consulting Unit (QCU) - Part of the Research Office at CSU. "QCU's core statistics support services include:
- General consultancy on statistical theory and application related to research problems
- Tutorials/training for learning and using analysis software such as R, SPSS, G*Power3.1, @Risk and Netica
- Advice on, support to, and completion of statistical analysis needed from research projects..."
Spacial Data Analysis Network (SPAN) - A research support unit within the Research Office, Charles Sturt University. SPANs primary role is to support research by academic staff and higher degree (HDR) students with:
- Spatial Statistics
- Simulation and Modelling
- Provision of spatial and analytical software
- Access to scientific instrumentation and other hardware
- Data sourcing and supply
SPAN can assist with Data Analysis and Visualisation of digital data sets, such as:
- Satellite or Airborne Imagery
- GIS and Mapping Data
- Census and other Australian Bureau of Statistics data
- Climate and Weather data from the Bureau of Meteorology
SPAN can help with access to detailed datasets from the Australian Bureau of Statistics and other government agencies.
Contact the staff at SPAN to find out more.
CSU Data storage
If you use cloud data storage eg.dropbox, or your local computer during analysis, you should back up your data on a secure Charles Sturt University (CSU) server. Apply for CSU secure storage through a DIT help desk request.
To estimate storage consider the size of similar data files you or colleagues will need to collect, use the graphic below as a guide. For extremely large data storage requirements (>1TB) arrangements should be made with DIT prior to commencing your Research Project.
Source: Charles Sturt University (Producer). (2017). Introduction to Research Data Management. Slide 17 [ELMO Training Module]
For more information: CSU Division of Information Technology Framework for Data Storage
Other Secure Data storage
See the Cloudstor knowledgebase for information on:
- S3 Gateway to data storage
- Cloudstor FileSender
- Cloudstor Rocke for fast file or machine created data uploads.
CSU Research Office guide to Cloud and Supercomputing Resources. Charles Sturt University is a member of Intersect, a not-for-profit organisation set up to support research in Australia. Intersect extends the technology services of the university and provides services, expert consulting and training programs.
The Australian Research Data Commons and NeCTAR - Has Virtual Machine Data storage products with allocations of 0GB to 480GB for different purposes. Datasets for a Research Project requiring continued processing and analysis will need a different storage product than a completed dataset for long-term preservation and sharing.
Managing sensitive data
The Australian National Data Service (ANDS) defines sensitive data as any data that can “be used to identify an individual, species, object, or location that introduces a risk of discrimination, harm, or unwanted attention.” ANDS is now a participant in the Australian Research Data Commons.
The things you may need to consider in regard to sensitive data are:
- Type of data
- Gaining consent and ethics approvals
- Legal requirements
- Processes that will keep the data confidential
- Processes that will de-identify the data
- Storage of the sensitive data
- Conditions that may be placed upon the access to the data
- Licensing of the data
Further information about these and other issues on the ANDS website on sensitive data.