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

Repository of NSF Funded Publications and Data Sets: "Back of Envelope" 15 year Cost Estimate

In this back of envelope study we calculate the 15 year fixed and variable costs of setting up and running a data repository (or database) to store and serve the publications and datasets derived from research funded by the National Science Foundation (NSF). Costs are computed on a yearly basis using a fixed estimate of the number of papers that are published each year that list NSF as their funding agency. We assume each paper has one dataset and estimate the size of that dataset based on experience. By our estimates, the number of papers generated each year is 64,340. The average dataset size over all seven directorates of NSF is 32 gigabytes (GB). A total amount of data added to the repository is two petabytes (PB) per year, or 30 PB over 15 years. The architecture of the data/paper repository is based on a hierarchical storage model that uses a combination of fast disk for rapid access and tape for high reliability and cost efficient long-term storage. Data are ingested through workflows that are used in university institutional repositories, which add metadata and ensure data integrity. Average fixed costs is approximately $.0.90/GB over 15-year span. Variable costs are estimated at a sliding scale of$150 - $100 per new dataset for up-front curation, or$4.87 – $3.22 per GB. Variable costs reflect a 3 The total projected cost of the data and paper repository is estimated at$167,000,000 over 15 years of operation, curating close to one million of datasets and one million papers. After 15 years and 30 PB of data accumulated and curated, we estimate the cost per gigabyte at $5.56. This$167 million cost is a direct cost in that it does not include federally allowable indirect costs return (ICR). After 15 years, it is reasonable to assume that some datasets will be compressed and rarely accessed. Others may be deemed no longer valuable, e.g., because they are replaced by more accurate results. Therefore, at some point the data growth in the repository will need to be adjusted by use of strategic preservation

Keeping Research Data Safe 2: Final Report

The first Keeping Research Data Safe study funded by JISC made a major contribution to understanding of long-term preservation costs for research data by developing a cost model and indentifying cost variables for preserving research data in UK universities (Beagrie et al, 2008). However it was completed over a very constrained timescale of four months with little opportunity to follow up other major issues or sources of preservation cost information it identified. It noted that digital preservation costs are notoriously difficult to address in part because of the absence of good case studies and longitudinal information for digital preservation costs or cost variables. In January 2009 JISC issued an ITT for a study on the identification of long-lived digital datasets for the purposes of cost analysis. The aim of this work was to provide a larger body of material and evidence against which existing and future data preservation cost modelling exercises could be tested and validated. The proposal for the KRDS2 study was submitted in response by a consortium consisting of 4 partners involved in the original Keeping Research Data Safe study (Universities of Cambridge and Southampton, Charles Beagrie Ltd, and OCLC Research) and 4 new partners with significant data collections and interests in preservation costs (Archaeology Data Service, University of London Computer Centre, University of Oxford, and the UK Data Archive). A range of supplementary materials in support of this main report have been made available on the KRDS2 project website at http://www.beagrie.com/jisc.php. That website will be maintained and continuously updated with future work as a resource for KRDS users

Incremental scoping study and implementation plan

This report is one of the first deliverables from the Incremental project, which seeks to investigate and improve the research data management infrastructure at the universities of Glasgow and Cambridge and to learn lessons and develop resources of value to other institutions. Coming at the end of the project’s scoping study, this report identifies the key themes and issues that emerged and proposes a set of activities to address those needs. As its name suggests, Incremental deliberately adopts a stepped, pragmatic approach to supporting research data management. It recognises that solutions will vary across different departmental and institutional contexts; and that top-down, policy-driven or centralised solutions are unlikely to prove as effective as practical support delivered in a clear and timely manner where the benefits can be clearly understood and will justify any effort or resources required. The findings of the scoping study have confirmed the value of this approach and the main recommendations of this report are concerned with the development and delivery of suitable resources. Although some differences were observed between disciplines, these seemed to be as much a feature of different organisational cultures as the nature of the research being undertaken. Our study found that there were many common issues across the groups and that the responses to these issues need not be highly technical or expensive to implement. What is required is that these resources employ jargon-free language and use examples of relevance to researchers and that they can be accessed easily at the point of need. There are resources already available (institutionally and externally) that can address researchers’ data management needs but these are not being fully exploited. So in many cases Incremental will be enabling efficient and contextualised access, or tailoring resources to specific environments, rather than developing resources from scratch. While Incremental will concentrate on developing, repurposing and leveraging practical resources to support researchers in their management of data, it recognises that this will be best achieved within a supportive institutional context (both in terms of policy and provision). The need for institutional support is especially evident when long-term preservation and data sharing are considered – these activities are clearly more effective and sustainable if addressed at more aggregated levels (e.g. repositories) rather than left to individual researchers or groups. So in addition to its work in developing resources, the Incremental project will seek to inform the development of a more comprehensive data management infrastructure at each institution. In Cambridge, this will be connected with the library’s CUPID project (Cambridge University Preservation Development) and at Glasgow in conjunction with the Digital Preservation Advisory Board

The case for joined-up research on carbon emissions from the building stock: adding value to household and building energy datasets

To reach UK objectives for reducing carbon emissions, it is argued that joined-up research on energy use in buildings is essential to develop and support government policy initiatives. The performance based approach introduced in Part-L of the 2006 Building Regulations has further underlined the role of coordinated research to monitor their effectiveness and provide feedback for subsequent revisions. Unfortunately, differences in dwelling classifications systems used in major household surveys currently hinder much of the supporting analysis that might improve SAP and other energy models. The Carbon Reduction in Buildings project has begun a process of integrating or organising existing building energy datasets into a coherent structure for the domestic sector. In addition, it is proposed to archive these for researchers via a building data repository that would facilitate joined-up research more widely

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

Business process prioritization criteria: a case study in the financial market

Purpose – This paper aims to analyze and examine how an organization from the financial sector prioritizes its business processes and what criteria are adopted to select the most appropriate process for improvement projects.Design/methodology/approach – This descriptive research is based on an exploratory approach. Qualitative methodology was applied to a case study through on-site observation, documentation analysis and semi-structured interviews.Findings – The results confirm criteria mentioned in the literature, such as financial aspects and strategic impacts, but also raised a new critical issue: automation potential of manual processes, reflecting the current movement of process automation.Research limitations/implications – As a limitation of this study, it is worth mentioning the application in only one organization in the financial market and the small number of respondents, even though they occupy leadership positions in the organization.Practical implications – As a practical implication, the present work offers a direction for managers of the financial sector in structuring and applying models for prioritizing processes aimed at organizational efficiency.Social implications – Automation solutions for process improvement need careful study to minimize impacts in human resources reduction. In this sense, the eligibility of a process for automation must be carefully considered.Originality/value – This paper presents the evolution of the process prioritization model adopted by a large institution in the financial market, which has a significant presence in the Brazilian and international markets as a commercial and wholesale bank

Libraries and the management of research data

A discussion of the role of university libraries in the management of digital research data outputs. Reviews some of the recent history of progress in this area from a UK perspective, with reference to international developments