The Tri Agencies are developing policies around Research Data Management. The Objectives are to improve efficiency of research, support re-use of data for new insights or discoveries, foster collaboration and facilitate greater transparency.
2016: Statement of Principles on Digital Data Management. To promote excellence in digital data management practices and data stewardship in agency-funded research. " All data need to be managed, but not all data need to be shared or preserved"
2017: Draft Policy on Data Management Plans Contains three elements expected to be policy by 2018-19:
1. Institutional Plans 2. Data Management Plans 3. Data Deposit
Template for an Institutional Strategy for Research Data Management There are five major components of an institutional strategy:
1. Awareness raising on campus of research data stewardship Researchers’ perspectives towards data sharing are varied, as are levels of expertise in terms of good data management practices. There is a need to raise awareness with the research community about the benefits and practices for good data management, as well as impending policy requirements.
2. Assessment of institutional readiness
2.1. Undertake a review of the current data landscape on campus To effectively manage data holdings and fully realize their potential, an organisation must first be aware of the location, condition and value of its assets. Institutions can undertake a review the data landscape on campus, using one of a number of existing tools.
2.2. Assess existing data management capacity and resources Data stewardship requires the management of data across the life cycle, with the onus of responsibility transferred between various stakeholders over time. Long term stewardship of research data will entail an expanded role for institutions in the data management landscape and involve greater investments in expertise and services. Assessing current strengths and gaps in data management capacity will help the institution develop a plan for improving capacities in three key areas:
2.2.1. Data management plans DMPs are formal documents that state what data will be created and how, and outline the plans for sharing and preservation, noting what is appropriate given the nature of the data and any restrictions that may need to be applied. DMPs are a way of improving data management practices of researchers and help establish how data will be managed in advance of a project. DMPs are increasingly expected by funders and it is likely that the Tri-Agency data policy will include a requirement for researchers to submit a DMP with their proposal.
2.2.2. Institutional support and expertise Appropriate management of research data requires expertise across the data lifecycle, including data management planning, data collection and analysis, as well as providing access and preservation of data once the project is complete.
2.2.3. Data repositories and archiving The role of the institutions in the data management landscape is becoming increasingly important. While some researchers have access to domain or disciplinary archives, many do not. Research libraries, many of which are already managing data, will likely need expand these services to provide greater coverage, while also linking into national and international networks.
3. Institutional policy statement A data policy or statement provides a framework to help researchers and other stakeholders on campus understand their roles and responsibilities around managing research data. This could be a stand alone policy, or a statement that is integrated into an institution's research policy and should be aligned with related policies and requirements of funders.
4. Local training and support activities Studies and surveys have shown that many researchers lack the expertise and resources to properly manage their data, and to create comprehensive data management plans. Training for researchers will be important to ensure that data are properly managed and ready for re-use. Local training can be supported by and link into other national and domain based activities.
5. Institutional roadmap A realistic roadmap will help institutions build capacity for research data management over the medium term. This will ensure that institutions are able to adhere to research data management requirements and continue to improve institutional capacity for research data management activities. The roadmap will contain the following components, based on information gathered through earlier stages of the institutional strategy:
1. What do we do now and what do we have in place?
2. What must we do to meet any identified gaps?
3. When will we do it?
4. What resources will we commit?
Research Data: Data that are used as primary sources to support technical or scientific enquiry, research, scholarship, or artistic activity, and that are used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results.
Research Data Management – Data Management refers to the storage, access and preservation of data produced from a given investigation. Data management practices cover the entire lifecycle of the data, from planning the investigation to conducting it, and from backing up data as it is created and used to long term preservation of data deliverables after the research investigation has concluded.