Draft DCC Curation Lifecycle Model

Lifecycle management of digital materials can help conceptualisation of the stages required to successfully curate digital material. A number of discipline specific models, and more generally applicable standards, have been developed which can be used as a basis when planning and implementing organisational management of digital material. The generic Draft DCC Curation Lifecycle Model identifies curation actions applicable either across the whole digital lifecycle or sequentially throughout it. Domain specific models, with greater granularity, will be developed to ensure readily accessible advice

Education alignment

This essay reviews recent developments in embedding data management and curation skills into information technology, library and information science, and research-based postgraduate courses in various national contexts. The essay also investigates means of joining up formal education with professional development training opportunities more coherently. The potential for using professional internships as a means of improving communication and understanding between disciplines is also explored. A key aim of this essay is to identify what level of complementarity is needed across various disciplines to most effectively and efficiently support the entire data curation lifecycle

Digital curation and the cloud

Digital curation involves a wide range of activities, many of which could benefit from cloud deployment to a greater or lesser extent. These range from infrequent, resource-intensive tasks which benefit from the ability to rapidly provision resources to day-to-day collaborative activities which can be facilitated by networked cloud services. Associated benefits are offset by risks such as loss of data or service level, legal and governance incompatibilities and transfer bottlenecks. There is considerable variability across both risks and benefits according to the service and deployment models being adopted and the context in which activities are performed. Some risks, such as legal liabilities, are mitigated by the use of alternative, e.g., private cloud models, but this is typically at the expense of benefits such as resource elasticity and economies of scale. Infrastructure as a Service model may provide a basis on which more specialised software services may be provided. There is considerable work to be done in helping institutions understand the cloud and its associated costs, risks and benefits, and how these compare to their current working methods, in order that the most beneficial uses of cloud technologies may be identified. Specific proposals, echoing recent work coordinated by EPSRC and JISC are the development of advisory, costing and brokering services to facilitate appropriate cloud deployments, the exploration of opportunities for certifying or accrediting cloud preservation providers, and the targeted publicity of outputs from pilot studies to the full range of stakeholders within the curation lifecycle, including data creators and owners, repositories, institutional IT support professionals and senior manager

Supporting emerging researchers in data management and curation

While scholarly publishing remains the key means for determining researchers’ impact, international funding body requirements and government recommendations relating to research data management (RDM), sharing and preservation mean that the underlying research data are becoming increasingly valuable in their own right. This is true not only for researchers in the sciences but also in the humanities and creative arts as well. The ability to exploit their own - and others’ - data is emerging as a crucial skill for researchers across all disciplines. However, despite Generation Y researchers being ‘highly competent and ubiquitous users of information technologies generally’ they appears to be a widespread lack of understanding and uncertainty about open access and self-archived resources (Jisc study, 2012). This chapter will consider the potential support that academic librarians might provide to support Generation Y researchers in this shifting research data landscape and examine the role of the library as part of institutional infrastructure. The changing landscape will impact research libraries most keenly over the next few years as they work to develop infrastructure and support systems to identify and maintain access to a diverse array of research data outputs. However, the data that are being produced through research are no different to those being produced by artists, politicians and the general public. In this respect, all libraries - whether they be academic, national, or local - will need to be gearing up to ensure they are able to accept and provide access to an ever increasing range of complex digital objects

Fostering open science practice through recognising and rewarding research data management and curation skills

In a bid to improve research integrity, drive innovation, increase knowledge and to maximize public investment, researchers are increasingly under pressure to work in a more open and transparent way. This movement has been referred to as open science. Open science offers a range of potential and measurable benefits – for researchers and the institutions that employ them as well as for society more generally. However, to realise these benefits, we must work towards changing current research practices and behaviours. Researchers will need to acquire new research data management and curation skills that enable them to undertake a broader range of tasks along the entire research lifecycle – from undertaking new means of collaboration, to implementing data management and sharing strategies, to understanding how to amplify and monitor research outputs and to assess their value and impact. In parallel, information professionals who work to support researchers and the open science process will also need to expand their research data management and curation skillsets. It will be equally important that current recognition and reward systems are amended to reflect the application of such skillsets within a range of disciplines. This paper will explore the potential role that librarians can play in supporting and progressing open science and discuss some of the new skills that librarians may require if they are to fulfil this role effectively. Citing examples from the current UK research landscape, this paper will map these skills to the Wellcome Trust and Digital Science’s CRediT Taxonomy which was developed in 2013 to enable the broad range of contributions involved in producing research outputs to be more consistently described and rewarded

KAPTUR: exploring the nature of visual arts research data and its effective management.

KAPTUR (2011-2013), funded by JISC and led by the Visual Arts Data Service (VADS), is a highly collaborative project involving four institutional partners: the Glasgow School of Arts; Goldsmiths, University of London; University for the Creative Arts; and the University of the Arts London. The preservation and publication of research data is seen as positive and all UK Research Councils now require it as a condition of funding (RCUK 2012). As a result a network of data repositories are emerging (DataCite 2012a), some funded by Research Councils, others by institutions themselves. However, research data management practice within the visual arts appears ad hoc. None of the specialist arts institutions within the UK has implemented research data management policies (DCC 2011a), nor established research data management systems. KAPTUR seeks to investigate the nature of visual arts research data, making recommendations for its effective management; develop a model of best practice applicable to both specialist arts institutions and arts departments in multidisciplinary institutions; and apply, test and refine the model with the four institutional partners. This paper will explore the nature of visual arts research data and how effective data management can ensure its long term usage, curation and preservation

DATUM in Action

This collaborative research data management planning project (hereafter the RDMP project) sought to help a collaborative group of researchers working on an EU FP7 staff exchange project (hereafter the EU project) to define and implement good research data management practice by developing an appropriate DMP and supporting systems and evaluating their initial implementation. The aim was to "improve practice on the ground" through more effective and appropriate systems, tools/solutions and guidance in managing research data. The EU project (MATSIQEL - (Models for Ageing and Technological Solutions For Improving and Enhancing the Quality of Life), funded under the Marie Curie International Research Staff Exchange Scheme, is accumulating expertise for the mathematical and computer modelling of ageing processes with the aim of developing models which can be implemented in technological solutions (e.g. monitors, telecare, recreational games) for improving and enhancing quality of life.1 Marie Curie projects do not fund research per se, so the EU project has no resources to fund commercial tools for research data management. Lead by Professor Maia Angelova, School of Computing, Engineering and Information Sciences (SCEIS) at Northumbria University, it comprises six work packages involving researchers at Northumbria and in Australia, Bulgaria, Germany, Mexico and South Africa. The RDMP project focused on one of its work packages (WP4 Technological Solutions and Implementation) with some reference to another work package lead by the same person at Northumbria University (WP5 Quality of Life). The RDMP project‟s innovation was less about the choice of platform/system, as it began with existing standard office technology, and more about how this can be effectively deployed in a collaborative scenario to provide a fit-for-purpose solution with useful and usable support and guidance. It built on the success of the Datum for Health project by taking it a stage further, moving from a solely health discipline to an interdisciplinary context of health, social care and mathematical/computer modelling, and from a Postgraduate Research Student context to an academic researcher context, with potential to reach beyond the University boundaries. In addition, since the EU project is re-using data from elsewhere as well as creating its own data; a wide range of RDM issues were addressed. The RDMP project assessed the transferability of the DATUM materials and the tailored DATUM DMP

D3.2 Cost Concept Model and Gateway Specification

This document introduces a Framework supporting the implementation of a cost concept model against which current and future cost models for curating digital assets can be benchmarked. The value built into this cost concept model leverages the comprehensive engagement by the 4C project with various user communities and builds upon our understanding of the requirements, drivers, obstacles and objectives that various stakeholder groups have relating to digital curation. Ultimately, this concept model should provide a critical input to the development and refinement of cost models as well as helping to ensure that the curation and preservation solutions and services that will inevitably arise from the commercial sector as ‘supply’ respond to a much better understood ‘demand’ for cost-effective and relevant tools. To meet acknowledged gaps in current provision, a nested model of curation which addresses both costs and benefits is provided. The goal of this task was not to create a single, functionally implementable cost modelling application; but rather to design a model based on common concepts and to develop a generic gateway specification that can be used by future model developers, service and solution providers, and by researchers in follow-up research and development projects.<p></p> The Framework includes:<p></p> • A Cost Concept Model—which defines the core concepts that should be included in curation costs models;<p></p> • An Implementation Guide—for the cost concept model that provides guidance and proposes questions that should be considered when developing new cost models and refining existing cost models;<p></p> • A Gateway Specification Template—which provides standard metadata for each of the core cost concepts and is intended for use by future model developers, model users, and service and solution providers to promote interoperability;<p></p> • A Nested Model for Digital Curation—that visualises the core concepts, demonstrates how they interact and places them into context visually by linking them to A Cost and Benefit Model for Curation.<p></p> This Framework provides guidance for data collection and associated calculations in an operational context but will also provide a critical foundation for more strategic thinking around curation such as the Economic Sustainability Reference Model (ESRM).<p></p> Where appropriate, definitions of terms are provided, recommendations are made, and examples from existing models are used to illustrate the principles of the framework