Many funders and publishers, as well as institutions, require researchers to make arrangements for data management and an appropriate level of sharing.
Internationally there is a growing trend for funders to mandate data management and sharing (e.g. National Science Foundation and the National Institute of Health) and Australia will soon follow. You can use Sherpa Juliet to check funder's conditions for open data archiving.
This section of the Toolkit outlines the data management requirements for Australia's major funders, the NHMRC and ARC.
National codes and international policies and principles (such as F.A.I.R. data) provide a framework for thinking about data management and sharing and inform funder policies and best practice.
Key points from the NHMRC Open Access Policy:
"The NHMRC acknowledges the importance of making research data publicly accessible and therefore strongly encourages researchers to consider the reuse value of their data and to take reasonable steps to share research data and associated metadata arising from NHMRC supported research."
When sharing data, researchers should ensure that appropriate metadata accompany the datasets. This will allow users of the data to fully understand the data, the curation strategies, assumptions, experimental conditions and any other details relevant to the interpretation of the data. See the Documentation and Metadata section of the Toolkit for more information.
When sharing research data, researchers must also consider the appropriate level of access that they would like to provide to users. The level of access may range from highly restricted (e.g. commercial in confidence, patient level, culturally sensitive, national security) to fully open access. See the Sharing Sensitive Data section of the Toolkit for more information.
The NHMRC does not currently require a data management plan. However, NHMRC encourages researchers to conduct data management planning activities as a matter of best practice and as a means to facilitate F.A.I.R. data
Since February 2014, the ARC has required researchers to briefly outline how they plan to manage research data arising from ARC-funded research in their grant applications, as specified in the funding rules for Discovery and Linkage programs. Further guidance is available via the Instructions and Frequently Asked Questions for each scheme:
"The Project Description requires researchers to articulate briefly their plans for the management of data generated through the proposed Project. In answering this question researchers need not include extensive detail of the physical or technological infrastructure. Answers should focus on plans to make data as openly accessible as possible for the purposes of verification and for the conduct of future research by others. Where it may not be appropriate for data to be disseminated or re-used, justification may be provided."
Plans should include but are not limited to storage, access and re-use arrangements and the text is not expected to be more than half a page. It is not sufficient to state that the institution has a data management policy.
Currently, the ARC does not require full, detailed data management plans (such as those required by some funding agencies internationally) and does not mandate open access to data. They do however, consider data management planning an important part of the responsible conduct of research and strongly encourage the deposition of data arising from a Project in an appropriate publicly accessible subject and/or institutional repository.
Australian funder requirements are informed by policies, strategies and principles including:
More resources on the FAIR Data Principles:
An inherent principle of publication is that others should be able to replicate and build upon the authors' published claims. Many journals now require you to make your data available without undue qualification, for example:
Major publishers like Wiley and Springer have a range of data sharing policy types across their journals - from encouraging data sharing to mandating it - but there is a growing trend is towards the latter.
Some publishers (for example Wiley) include author compliance tools for checking these requirements.
Some researchers may also choose to publish a data paper.
Data journals are publications whose primary purpose is to expose datasets. This may be considered best practice for researchers whose primary output is data. It includes an element of peer review, maximizes opportunities for re-use and attracts academic accreditation for data scientists as well as front-line researchers.
See the ANDS Guide on Data and Journals for more information. As the guide notes:
"While individual publisher policies vary, it's worth noting that publishing data through a data journal does not necessarily prevent the publication of data analyses and research results in a traditional journal, along with a reference and links to the data journal paper. This provides readers with access to all relevant information about a piece of research and may result in citation of both the journal article and data paper."
Check out some examples of data papers by JCU researchers in ResearchOnline@JCU
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