Research digital skills training 2021

Research Data

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Research data management (RDM) infrastructure is seen as an essential part of research support within the University. RDM infrastructure will assist researchers to store, manage, disseminate, curate, publish, catalogue, and archive data generated by research.

Research Data Management/Storage

The term research data encompasses the entire gamut of output generated by research at the University of Auckland. It may include anything created, collected, and/or generated in the course of research. RDM services facilitate the discovery, access, and reuse of research data. Proper leverage of RDM supports collaboration and enhances research impact. Additionally, funding bodies are increasingly requiring researchers to explicitly state their RDM approach in the form of data management plans (DMPs). The Centre for eResearch is working with both the Libraries and Learning Services, and IT Services to develop and promote the RDM knowledge and tools necessary to produce rigorous, high-impact research.

Data Transfer

The Centre for eResearch offers consultations to researchers who need assistance with transferring big data (~TBs) to and from the University to external sites or repositories. Experts are available to meet with you and discuss your specific use case, and provide a solutions that cater to your needs and requirements. The Centre for eResearch can also help with general network solutions needed to progress your research.

Data Publishing

The Data Publishing and Discovery Service (figshare for institutions) was officially launched in 2017, primarily to support data publishing. Research data descriptions and documentation (or metadata) supports discovery and reuse. All published items are assigned a persistent digital identifier (DOI) through DataCite and indexed by Google. For data that is sensitive, embargoed or otherwise restricted in access (e.g. copyright) one can make use of metadata only records, private links and/or reserve DOI features. Published items can be embedded in other web pages e.g. CANVAS.

Data hosting: All data published to our institutional figshare are stored locally on the University of Auckland storage systems.
Eligibility: Doctoral candidates, staff, and external collaborators.
Requirements: An “auckland.ac.nz” email address and a University of Auckland username and login.
Cost: There is no direct cost to researchers, research groups or faculties.
Support: General support is provided through the staff at the Centre for eResearch and the Libraries and Learning Services.

Workshops

In partnership with Libraries and Learning Services, the Centre for eResearch offers regular workshops aimed at PhD candidates, supervisors, principal investigators, and research staff to support research data management, data management planning and data publishing. For a training programme schedule, search Library workshops for ‘rdm’

For more information, including requests for workshops or presentations for research groups/departments, or technical support for uploading datasets larger than 100GB, please contact researchdata@auckland.ac.nz.

Instrument Data

The Centre for eResearch in partnership with faculty IS and central ITS offers support to researchers who seek effective data management solutions for their instrument data. We offer face-to-face consultations to understand the specific needs regarding your instrument data, and design solutions for storing, sharing, and transferring data. Where appropriate, we can also point to appropriate avenues for publishing and archiving instrument data.

Case studies of research data

Data maturity project in High Value Nutrition – National Science Challenge

Passive acoustic modelling

Given the current global biodiversity declines, understanding the process of biodiversity loss and improving tools for species conservation are emerging as major issues for ecological research. Protected areas are crucial for protecting biodiversity.

Research data publishing and preservation at COMPASS

The Centre of Methods and Policy Application in the Social Sciences (COMPASS) is interested in leveraging online data publishing to enhance research impact, teaching, and collaborative opportunities. It is currently using the University’s Data Publishing and Discovery Service (DPADS), namely Digital Science’s Figshare platform – a pilot program led by the University Library and Learning Services and the Centre for eResearch.

Improving diagnosis for schistosomiasis by using the ‘metabolic footprint’ of urine samples from an animal model of Schistosoma infection to identify possible biomarkers

Schistosomiasis infection constitutes a major public health problem, particularly in countries where the disease is endemic. Worldwide. It is estimated 779 million people at the risk of contracting schistosomiasis, while about 210 million are infected with the disease.

Taking a ‘Big Data’ approach to find new clinical-omic associations in cancer

Cancer is the number two cause of mortality in the OECD behind heart disease. Up until the late 1990’s, there was a concerted effort by drug companies to develop ‘blockbuster therapies’ for the treatment of cancer, i.e. cancer therapies developed with a one-size-fits-all approach.

Giving Pacific research greater reach

Launched in March 2016, the New Zealand Institute for Pacific Research (NZIPR) is a national institute aimed at promoting and supporting excellence in Pacific research, in order to deliver a world-class research programme focused on Pacific development, investment and foreign-policy issues.

Data management planning for MOA

MOA is a Japanese New Zealand collaboration that makes observations on dark matter, extra-solar planets and stellar atmospheres using the gravitational microlensing technique. The phenomenon now known as gravitational microlensing was first described by Einstein in 1936.

Making stroke recovery prediction tools freely available

Stroke is a leading cause of adult disability worldwide. Most people who experience a stroke have weakness on one side of their body. The ability to live independently again after stroke depends largely on the recovery of strength and function on the affected side.