Research data: lifecycle, plans and planning SEQld Data Intensive 29 th January 2015 Kathryn Unsworth

2 Research data management and lifecycle models… Why? [Data Lifecycle Models and Concepts Version 8]

A lifecycle within a lifecycle… ?  Because research activity often takes place in stages it can be viewed as forming a lifecycle  Data created or collected during a research project also go through various stages forming an interdependent lifecycle  However the data lifecycle often extends past the discrete nature of a research project 3

“Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers. Well organised, well documented, preserved and shared data are invaluable to advance scientific inquiry and to increase opportunities for learning and innovation.” 4

Some examples of research/data related lifecycle models 5

Processing data enter data, digitise, transcribe, translate check, validate, clean data anonymise data where necessary describe data manage and store data Analysing data interpret data derive data produce research outputs author publications prepare data for preservation Preserving data migrate data to best format migrate data to suitable medium back-up and store data create metadata and documentation archive data Giving access to data distribute data share data control access establish copyright promote data Re-using data follow-up research new research undertake research reviews scrutinise findings teach and learn manage/life-cycle

ANDS data curation continuum 7

Research lifecycle - JISC 8

The DCC Curation Lifecycle Model 9

United States Geological Survey Science (USGS) Data Lifecycle Model 10

When looking at planning to manage research data, it’s important to take into account both the research lifecycle and the data lifecycle; the intersections between the two are interdependent and critical to informing all stakeholders of their roles and responsibilities in context of the whole [research process]. That’s why I like the following model! 11

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OK, so what’s the practical use for these lifecycle models? 13

Lifecycle models provide a framework that informs planning for the management of data* 14 *Note: There is a difference between planning for the management of data and Data Management Plans

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Useful for planning future needs and capabilities and for helping define who in the institution does what, e.g.:  What assets and resources are required for data storage now and into the future?  What roles do the various business units in the institution play in support of describing, publishing and citation of data collections, and at what point in the lifecycle do these interventions occur? 16

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Data management plans are not mandatory inclusions in grant applications for Australian-funded research YET! 18

Scenario discussion: A glimpse into a probable future What is currently in place/missing at your institution to support researchers if data management plans were to become a compulsory part of grant applications, such as is currently the case for NSF- funded projects: NSF Data Management Plan Requirements Proposals submitted or due on or after January 18, 2011, must include a supplementary document of no more than two pages labelled “Data Management Plan”. This supplementary document should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results. See Grant Proposal Guide (GPG) Chapter II.C.2.j for full policy implementation. [Refer to handout]Grant Proposal Guide (GPG) Chapter II.C.2.j 19

20 This work is licensed under a Creative Commons Attribution 3.0 Australia License ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy (NCRIS).