All researchers produce data, whether ‘traditional’ numbers in graphs and tables, or primary research materials such as manuscripts, text, interview transcripts, or videos.
ResData is a service provided by the UNSW Library for UNSW researchers to manage their research data. The service has two main components:
- a platform for UNSW researchers to create and complete their Research Data Management Plans (RDMP)
- an online ResData catalog of UNSW datasets and collections of research materials
To create/edit a Research Data Management Plan, please login to Resdata.
The Management of Data section asks applicants to outline plans for the management of data produced as a result of the proposed research, including but not limited to storage, access, and re-use arrangements. Aside from the requirements of the Australian Code for the Responsible Conduct of Research, it is likely that projects which enable significant data to be shared with other researchers will be well received by assessors, so careful consideration of the plan to store, access and re-use the project’s data is required.
To address this heading, as a minimum, you should include statements such as the following:
- Data management practices will follow the principles of the Australian Code for the Responsible Conduct of Research.
- A research data management plan for the project will be established and managed using the UNSW ResData platform.
- All research data will be classified according to UNSW Classification Standards and handled in accordance to UNSW data handling guidelines.
- Research data obtained will be stored on a UNSW supported platform which is secured, managed and backed up centrally.
- Data will be archived using UNSW’s Data Archive (or another archive mechanism as applicable).
- At the conclusion of a project, Data will be made discoverable by registration on Research Data Australia (and/or discipline-specific registries where applicable).
Why is good data management important?
Effective data management underpins top level aims of good stewardship of public resources and responsible communication of research results.
Storing the right data in the right place protects staff, teaching and research work, and the University’s reputation. Failing to do so may mean re-creating documents, redoing experiments, paying to repeat procedures, hours of tedious administrative clean-up and searching through multiple backup tapes, or even retracting a published paper.
The benefits of good data management
Aside from complying with data storage requirements associated with institutional, funding and publishing bodies, there are many benefits to making the effort to establish strong research data management plans and adopt good data storage practices for your research activities.
UNSW Data Management Policies and Procedures
In addition to the Australian Code for the Responsible Conduct of Research, UNSW researchers must manage their data in accordance with the following policies and procedures:
- UNSW Research Code of Conduct
- Procedure for Handling Research Material and Data
- How long should records be kept? Retention periods for records relating to research
- How to destroy records
The UNSW Research Code of Conduct sets out requirements for minimum retention periods for data gathered as part of research conducted at the University. This means that for archival purposes, researchers must choose file formats and storage options that have the best chance of remaining accessible into the future.
Funding, Publication, and Compliance
UNSW researchers must comply with data management requirements embedded in relevant Australian and UNSW policies, as well as from funding bodies, publishers, and other organisations. Funding agencies increasingly require applicants to outline their data management plans (for data derived from the project) within the grant application.
- e.g. the ARC Discovery Program application now includes a ‘Management of Data’ section in which applicants must ‘outline plans for the management of data produced as a result of the proposed research, including but not limited to storage, access and re-use arrangements’.
- Compliance with the Australian Code for the Responsible Conduct of Research is a prerequisite for receipt of NHMRC funding.
Additionally, it is now becoming standard for publishers to require access to the data and associated metadata attached to a publication as part of the peer review process, or to publish as supplements.
The UNSW Research Data Management Plan (RDMP) is a document which enables UNSW researchers to consolidate and summarise information regarding the management of data for their research projects. An RDMP should be created prior to the start of the research project/activity.
The RDMP captures and records key information about your research, including:
- Project governance: project information; i.e. what is the project title, the FOR (Field of Research) codes, the funding information, and who are the project personnel (what is their role and required level of access)?
- Data organisation and documentation: data information; i.e. what sort of data are to be created or collected and how will data be managed?
- Ethics, privacy and confidentiality: i.e. what are the ethics approval details and characteristics of the data?
- Intellectual property, copyright and ownership: data ownership considerations; i.e. who owns the data and how can the data be used?
- Data storage: retention of data; i.e. what methods will be used to retain and store the data?
Collating the above details before you start working on your RDMP can help you complete the process more efficiently.
An RDMP is a living document – once it has been created, you can continue making changes to it if, and when, you need to. Your RDMP should always contain accurate information about your research and research team and be updated regularly to reflect all changes.
When do I need to complete a UNSW RDMP?
- Prior to the start of the research project
- To get access to the UNSW Data Archive: completion of an RDMP is a requirement for storage allocation.
- When applying for funding:
- Some funding agencies may require a data management plan as part of the grant application. The ARC Discovery Program application now includes a ‘Management of Data’ section in which applicants must ‘outline plans for the management of data produced as a result of the proposed research, including but not limited to storage, access, and re-use arrangements’.
- Note: if a funding agency requires a data management plan as part of the grant application, you, as the applicant, should refer to the funder’s specific requirements in the first instance.
How do I make one?
ResData is a Library service for managing UNSW research data. To create an RDMP you need to:
- Go to the ResData site (click HERE) and log in.
- Click "Plan a project" (for research staff) or "Plan HDR Project" (For HDR students)
- You can start with just a few mandatory fields......and update your plan as your project continues.
Once you have created and submitted your RDMP, and if you have selected the option to store your data using the UNSW Data Archive, your request will be assessed and authorized by the project’s Lead Chief Investigator (LCI).
Note: It is possible to create a plan without having a project in InfoED. In this case, you will need to complete some mandatory fields to create the RDMP.
The UNSW research data management plan (RDMP) is structured into five sections:
- Project details
- Data governance
- IP and copyright
- Data storage
- Data organisation
You need to complete the mandatory Part I (Project Details) and Part II (Data Governance) to submit your RDMP. An RDMP ID is generated when you click Save or Submit. This ID can be found on the ResData dashboard.
For parts III to V you may need more information about the research project and the data it will collect, generate or use. The RDMP is a living document which should be reviewed periodically and updated as the project progresses and/or changes.
Entering information in as many fields as possible at the beginning of the research project will prompt you to consider aspects of how you will manage data throughout the life of the project.
|You will Need||Also Consider|
If you have questions or would like assistance completing an RDMP please contact your Outreach Librarian.
UNSW has a Data Classification Standard for assessing data sensitivity, measured by the adverse impact a breach of the data would have upon UNSW.
The following matrix provides an aid for classifying your research data. For example, if your data contains culturally sensitive information, the data is classified as ‘Highly Sensitive’. If you have concerns about classifying your research data, you should contact Data Governance Team.
*Examples of research data classified as ‘Public’ include publicly available 3rd party datasets/Open Data, and information. However, once such datasets and information are manipulated (e.g., re-calculated, or annotated) in relation to your research project, it should be considered as unpublished research data, which is classified as ‘Private’ or higher.
For the purpose of selecting the data classification of your research data in the RDMP (Research Data Management Plan), please select the highest/most secure data classification level that applies to any portion of the data. For example, if the data has two portions classified as ‘Sensitive’ and ‘Highly Sensitive’ respectively, they can be handled based on their corresponding data classification. However, the project as a whole shall be classified as ‘Highly Sensitive’ in the RDMP.
UNSW Supported Storage Platforms for Corresponding Data Classifications
UNSW provides a number of approved data storage systems for our researchers.
- UNSW OneDrive (part of our Microsoft Licence) is suitable for project storage and collaborations. Data stored on UNSW OneDrive will be retained for at least 7 years.
- UNSW Data Archive is suitable for long-term storage of research data. Data stored on the Archive can be retained permanently. It is suitable for use from project start as it protects data files from deletion or changes.
- For a more comprehensive list of storage options please visit our Data Storage and Tools page.
We recommend seeking advice if:
- you need a solution to manage very large datasets
- you need a solution for highly sensitive data (medical, social, cultural etc)
- data storage costs are a significant factor in your grant.
For further advice on data systems or options to store data contact: firstname.lastname@example.org
Intellectual Property (IP)
Intellectual property (IP) relates to the property of your mind or intellect and includes knowledge, discoveries, and inventions in material form. It includes rights in respect of inventions, copyright, trademarks, designs, patents, plant breeder’s rights, circuit layouts, know-how, trade secrets, industrial designs, reports, publications, literary and artistic works.
Copyright is the right to reproduce, publish or distribute a work. Australian copyright law also applies to research data. Copyright cannot protect an individual ‘fact’ but can cover data compilations such as collections of sound and audio files, databases and data tables. A dataset or database can be protected by copyright if it:
- Provides intelligible information
- Has not been copied
- Has been produced using the independent intellectual effort and creativity of the researcher(s)
Clearly stating rights and permissions in data management plans and elsewhere helps ensure that data are cited correctly and reused appropriately. There may also be requirements from institutions and funding bodies that affect data ownership, IP and copyright.
Research data created at UNSW are subject to the Intellectual Property (IP) Policy. If pre-existing datasets are used they may have their own copyright and/or licensing agreement.
Licensing transfers some or all of the rights held by the copyright owner to a third party, such as a repository or an end user.
The Australian Copyright Council offers further information and answers to FAQs about Copyright and licensing.
Data documentation involves aspects such as:
- Data collection methodology and processes
- Variable-level documentation
- Directory structure
- Managing version control and file naming conventions
- Data confidentiality
- Access and use conditions
Metadata is structured information that describes, explains, locates, or makes it easier to retrieve, use, or manage an information resource. Metadata is often defined literally as ‘data about data’.
Organising, documenting and describing data means that data will be easier to locate in the future. It can also provide context for data and the research process.
A data management plan for a research project includes the following types of metadata:
Project aims, keywords, details of any metadata standards, controlled vocabularies or ontologies that are used to describe the data. These may be different depending on the research discipline concerned.
See the Digital Curation Centre’s page on Disciplinary Metadata for more information and links to different standards.
|Technical||Includes file formats and quality assurance processes such as calibration or validation|
|Administrative||Access conditions, copyright, ownership, file name conventions or directory structures|
|Provenance||Source or version of the data|
Research data are a valuable and important output of research. The collection or creation of research data often involves considerable time and effort.
Some data may have value that goes beyond the scope of the original project, which may not even be known at the time of the project.
- Collaborative research projects may also require data sharing across a group of people and/or institutions.
Spectrum of re-use
Data sharing and re-use can take a number of forms across a wide spectrum and sharing data doesn't necessarily mean providing unlimited access to all of your datasets.
Sharing data and data management
The ability of data to be effectively reused depends on good data management practices.
As part of a research data management plan, it is important to provide information that will allow the data to be accessed in the future, for re-use, to confirm or defend findings or to meet requirements from funding bodies or publishers. As part of the data management planning process, consider the following aspects of the project:
- What data will be retained and where will they be stored?
- How much (if any) of the data can be shared or published?
- Who should be the contact person for access enquiries?
- What sort of restrictions are there on access to data? For example, confidentiality of data that may be personally identifiable
- Is there a relevant Creative Commons licence that can define the terms of access and re-use?
Data citation is the practice of citing data sources for research publications, just as you would cite a paper in your own research work. This is a fairly new area, and organisations are still working out standards for identifying datasets.
What is a DOI
A DOI is a character string used to uniquely identify an object such as an electronic document. Metadata about the object is stored in association with the DOI name and may include a location, e.g. URL, where the object can be found. The DOI remains fixed over the lifetime of the document, whereas its location and other metadata may change. Referring to an online document by its DOI provides more stable linking than simply referring to it by its URL.
- Here is a typical example of a data citation using a DOI:
Irino, T; Tada, R (2009): Chemical and mineral compositions of sediments from ODP Site
127-797. Geological Institute, University of Tokyo. doi:10.1594/PANGAEA.726855.
Why is Data Citation Important
- As a researcher, you can receive attribution for publishing data through data citation
- Mandates from publishers to cite data may apply
- Mandates from institutions and funding bodies to make all research outputs open access may apply
- Data citation is part of the changing nature of scholarly publishing models, driven by the development of open access repositories (institutional and subject-oriented)
- Data citation engages with the collaborative spirit of sharing and reuse
At UNSW, the ResData system allows you to create a rich descriptive record of a dataset, and assign a Handle and/or DOI. Both identifiers will always resolve to a webpage which provides information about your dataset.
A number of external resources are available below with useful advice and information to assist you in managing your research data:
- Digital Curation Centre – What is digital curation? and How-to Guides & Checklists
- The University of Queensland – UQ Library – Research Data Management
- Oxford University – Infrastructure for Research Data Management:
- Figshare allows researchers to publish all of their data in a citable, searchable and shareable manner. All data is persistently stored online under the most liberal Creative Commons license, waiving copyright where possible. This allows scientists to access and share the information from anywhere in the world with minimal friction.
- Top 10 Mistakes in Data Management
Data documentation and metadata
These resources provide further details and advice about data documentation and metadata:
- MIT Libraries have an easily understandable list of ‘things to document’ in a research project
- Metadata (ANDS)
- Metadata guide
- Metadata (Boston University)
Sharing and Reuse
For information regarding the research data management, please contact us at: email@example.com