JISC Managing Research Data 2011-1013Programme Launch Meeting1st and 2nd December 2011

The National College of School Leadership, Nottingham

Agenda

DAY ONE -1st December 2011

ITime IActivity-1-'R--·-t-t·---------------------------

112.30 I rnva s egls ra Ion

13.30 Lunch30- Introduction to the programme and the workshop (Simon Hodson)

14·00 An overview of the programme and strand objectives; timescales and key events; objectives and activities· for the Launch Workshop.IUKOLN, 'Blogging Practices To Support Project Work' (Brian Kelly)

15.00- Parallel Sessions16.30

1) DCC Tools:DCC-run workshop giving introductions to and walk-throughs of various DCC tools; with time for discussionand sharing experiences.

• Data Asset Framework (DAF)• DMPonline• CARDIO

2) UMF Tools and Demos:Introductions and demos from the JANET Brokerage, from Eduserv and from UMF Research DataManagement Software as a Service Projects

• JANET Brokerage• Eduserv Cloud Pilot• BRISSkit Project• DataFlow Project• Smart Research Framework Project• VIDaaS Project• YouShare Project

Coffee Break

IPoster Session and Networking119.00 IDinner

DAY TWO - 2nd December 2011

rrime Activity09.00- Benefits Evidence Gathering in the JISC Managing Research Data Programme, 2011-1310.30

Simon Hodson, Neil Beagrie, Evidence GatherersThis session will look at approaches for gathering evidence of benefits and impact which will be important forprojects and the programme, both in making a case for investment in research data management andcontributing towards project business cases.

Simon Hodson will introduce the session and provide an overview of the work in the previous programme_ andthe role of the Programme Evidence Gatherers.

Neil Beagrie will provide a practical introduction to the KRDS Benefits Analysis Toolkit.Practical sessions will allow projects to look in detail at the Benefits Framework Tool, the Value Chain ImpactsTool and consider what benefits the project is likely to generate and what evidence may be gathered.

10.30- Coffee Break11.0011.00- Thematic Parallel Sessions:12.30

These sessions will focus on lessons from the previous programme and related work in other institutions,which touches on a number of themes of relevance to the new projects:

A: Preparing a business case, development to service (June Finch, University of Manchester, MaDAM andMiSS Projects); identifying efficiency benefits, cost savings (James Wilson, University of Oxford, Sudamih,VIDaaS and DaMaRO Projects).

B: Identifying and supporting researcher requirements; supporting researcher needs; evaluation (MeikPoschen, University of Manchester, MaDAM and MiSS Projects; Jonathan Tedds, University of Leicester,HALOGEN and BRISSkit Projects).

C: Policy development (Robin Rice, University of Edinburgh and Miggie Pickton, University of Northampton);guidance and training materials (Sarah Jones, DCC; Laura Molloy, HATII, University of Glasgow)

D: Data management planning and meeting funder requirements (Julie McLeod, University of Northumberland;Mansur Darlington, University of Bath; Brian Hole, UCL). The presentations and discussion in this session willfocus on a) disciplinary challenges as uncovered in requirements analyses, previous work and to what extentthese relate to funder requirements and/or other drivers; b) use and adaptation of the DCC's DMP checklistand/or DMPonline as a starting point; and c) how to go about supporting the execution of the plan and turning itinto practice.

12.30- Lunch13.3013.30- Thematic Parallel Sessions TBA15.00

15.00-15.30

These thematic sessions will be identified during the poster session. They may focus on disciplinary issues,though platforms and approaches may also be covered. For example, there are clusters of projects in thefollowing areas: health and life sciences, engineering, archaeology, social sciences, arts and humanities.Equally, projects may wish to initiate discussions around common technical approaches: repository platforms,use of SWORD2, metadata strategies and DataCite. These sessions will come up with actions to beannounced in the wrap-up (even if this is simply at the level of 'this group will keep in touch and exchangenews of progress').Wrap-up

JISC Managing Research Data 2011-1013Programme Launch Meeting1st and 2nd December 2011

The National College of School Leadership, Nottingham

Delegate List

Stelios Alexandrakis Systems Architect UCL Institute of Child HealthDavid Alien Research Project Manager University of BathBhavana Ananda Com"puting Officer University of OxfordChris Awre Head of Information Management University of HullNeil Beagrie Director Charles Beagrie LimitedAndrew Burnham Senior IT Research Liaison Manager University of Leicester

Dorothy Byatt Project Manager/Academic Liaison University of SouthamptonLibrarianYutong Cai Research Assistant Imperial College London

Chris Cannam Software Developer Centre for Digital Music, QueenMary University of London

Anna Clements Data Architect University of St AndrewsBrian Clifford Deputy Librarian University of LeedsGareth Cole Data Curation Officer University of Exeter

Anna Collins Research Data and Digital Curation Cambridge University LibraryOfficer

Amanda Conway Services Development Librarian University of the West ofEngland

Jez Cope ICT Project Manager University of BathLouise Corti Associate Director UK Data ArchiveShirley Crompton Senior Applications Developer STFC

Jennifer Crossley Research and Knowledge Exchange University of the West ofLibrarian England

Vasa Curcin Research Fellow ComputingMansur Darlington IdMRC Centre Manager University of BathJoy Davidson Associate Director Digital Curation Centre (DCC)

SONEX Workgroup forPablo de Castro SONEX Chair Scholarly Output Notification

and ExchangeGerry Devine Computer Scientist University of Reading

Matthew Dovey Programme Director, Digital JISCInfrastructures (Research)

Monica Duke Institutional Support Officer Digital Curation Centre(UKOLN, University of Bath)

Tom Ensom Project Officer UK Data ArchiveJill Evans Open Access & Data Curation Manager University Of Exeter

Marco Fabiani Post-doe research assistant C4DM - Queen Mary Universityof London

June Finch MeRC Programme Manager MeRC, University ofManchester

Katherine Fletcher Project Manager University of OxfordDavid Ford Chief Technology Officer University of HertfordshireKevin Ginty Research Project Manager University of SunderlandMarie- Gramstadt Kaptur Project Manager Visual Arts Data ServiceTherese (VADS)

Stephen Gray Technical Support Officer - Moving JISC Digital MediaImagesNorman Gray Researcher University of GlasgowRichard Green Consultant University of HullSteve Hitchcock Project Manager University of SouthamptonSimon Hodson Programme Manager JISC ExecutiveBrian Hole REWARD Project Manager Ubiquity Press

Bill Hubbard Head of Centre for Research The University of NottinghamCommunicationsNick Jackson Web Developer University of LincolnTom Jackson Research Manager University of YorkSarah Jones Research Officer DCC, University of Glasgow

Rachel Kane Research Data Management Project The University of SheffieldManagerBrian Kelly UKWeb Focus UKOLN, University of Bath

Simon Kerridge Head of Graduate Research Support I University of Sunderland IDirector ARMA

Mary McDerby Research Applications and Collaboration University of ManchesterOfficerStephen McGough Research Manager Newcastle UniversityJulie McLeod Professor Northumbria UniversityJohn Milner Programme Manager JANET(UK)Laura Molloy Researcher HAT11, University of GlasgowTim Parkinson Principal Software Consultant University of SouthamptonCharlotte Pascoe Data Scientist NCAS BADCMiggie Pickton Research Support Specialist The University of NorthamptonRichard Plant Project Officer University of Sheffield

Meik Poschen Research Associate MeRC, University ofManchester

Andy Powell Research Programme Director EduservSimon Price Acting Deputy Director ILRT, University of BristolRobin Rice Data Librarian EDINAJulian Richards Director Archaeology Data ServiceAnastasia Sakellariadi Research Data Scientist Ubiquity PressDavid Shotton 1 Emeritus Reader in Image Bioinformatics University of Oxford

Carlos Silva Planning and Development Manager University for the Creative Arts -VADS

Michael Soljak Clinical Research Fellow Imperial College LondonPaul Stainthorp Electronic Resources Librarian University of LincolnJonathan Tedds Senior Research Liaison Manager University of LeicesterAnthony Thomas Analyst Programmer UCLAngus Whyte Snr Institutional Support Officer Digital Curation Centre

Caroline Williams Director of Research and Learning University of NottinghamResourcesJames A J Wilson Project Manager University of OxfordJoss Winn Senior Lecturer University of LincolnLindsay Wood Project Officer Newcastle UniversityBill Worthington Project Manager University of Hertfordshire

Eleagrie

I Benefits Metrics for InstitutionsI • New research grant incomeiI • Number of research dataset publications generatedII • Number of research papersIJ • Improvements over time in benchmark results (e.g. repeats of AIDA benchmarking or surveys ofI awareness of relevant support services 'or funder requirements)

• Cost savings/efficiencies for central services and/or departments

• Re-use of infrastructure in new projects

i Benefits Metrics for Researchers/Research TeamsIII • Increase in grant income/success ratesI • Increased visibility of research through data citationIi • Percentage improvement in routine back-up of dataII • Average reduction in waiting time (time latency) for data requests

I • Average time saved in research data management and grant proposal activitiesIi • Percentage improvement in range/effectiveness of research tool/softwareii Benefits Metrics for Research Support Services!I • Percentage of potential user community that takes up servicesi

• Number of data deposits with a repository

• Number of downloads of a dataset(s) within a repository

• Activity based costing methods (e.g. using KRDS activity model to benchmark activity based

costs over time)

• Results of user feedback forms

• Number of times different researchers collectively create/maintain a dataset via the repository

Benefits Metrics for Scholarly Communication and Access

• Number of citations to datasets in research articles

• Number of citations to specific methods for research data management

• Number of datasets deposited with enhanced metadata

• Percentage increase in user communities

• Number of service level agreements for nationally important datasets

Beagrie

Benefits for Institutions

• New research funding and research opportunities

• Sustainability of research data infrastructure

• Economies of scale

• Better and larger publication output

• Integrateq thinking around research data management

• Improved data management plans and policies

• Cost modelling to plan for increasing demand

• Prototyping technical solutions based on established technology

• Long-term road-maps for research data management

• Change to user practices

• Stimulating new networks and collaborations

• Mitigating organisational risks

• Increased awareness of funder requirements

• Sharing of exemplars, lessons learnt and best practice

• Impact and knowledge transfer

Benefits for Researchers/Research Teams

• Inspiration for new research

• Built-in support for research tasks

• Rapid access to results data and derived data

• Time and efficiency savings

• Increasing data management skills

Increased awareness of relevant support, services and tools

Enhancing finding and organizing of data

Reliable citations to their data

No loss of access to data as a result of Post Doc turnover

Guidance and training for researchers embedded in Graduate Schools

• Supporting collective data collection/creation practices

• No re-creation of existing data

• Improved version control

• Secure back-up and reduced risk of data loss

Benefits for Research Support Services

• Better knowledge of the research data landscape

• Better awareness of researchers' needs

• Re-use of infrastructure in other projects

• Enhancements of and guidance for existing tools and development of new ones

• Improved data storage and data management

• Streamlined and updated advice and guidance

• Code developers able to enhance research software and tools

• Reduced risks of errors from manual transcription between systems

• Enhanced long-term stewardship

• Lower current and future preservation costs

Benefits for Scholarly Communication and Access

• Support for data publishing

• Enhanced data sharing and discovery and incentives to deposit

• Overcoming fears of data abuse

• Reducing effort to present data

• Greater consistency and standards between projects to enable data re-use

• Comprehensiveness of available information

• Improved quality of research by locating better, more relevant information

• Improved quality of research by linking relevant materials

• Re-purposing data for new audiences

• Re-purposing of methodologies

• Protecting returns on earlier investments

• Support of nationally important datasets

Keeping Research Data Safe FactsheetCost issues in digital preservation of research data

This factsheet illustrates for institutionsJ researchersJ and funders some of the key findings and recommendationsfrom the JISC-funded Keeping Research Data Safe (KRDS1) and Keeping Research Data Safe 2 (KRDS2) projects.Further information on the research and findings can be found in the final reports and on the KRDS website.

What Costs Most?Acquisition and ingest costs most. The costs of archival storage andpreservation activities are consistently a very small proportion of the overallcosts and significantly lower than the costs of acquisition/ingest or accessactivities for all our case studies. Note we believe early preservation actionduring ingest or pre-ingest produces lower costs over the Iifecycle as a whole.(KRDSIJ p.25; KRDS2J pp.31-52)

Activity Costs for the Archaeology Data ServiceOutreach/ Acquisitionf Archival Storage and AccessIngest Preservationc. 55% c. 15% c. 31%

Recommendation toFundersC> From our research, it is likelythat the largest potential costefficiencies will come fromfuture tool developmentsupporting automation ofingest and access activities forcuration and preservation.(KRDS2 J p.83)

Impact of Fixed Costs• The costs of long-term data curation/preservation are dominated byfixed costs that do not vary with the size of the collections;

• Staff are the major cost component overall and there is a minimumbase-level of staff coverJ skills and equipment required for anyservice;

• Activities characterised by significant fixed costs can reduce the per-unit cost of long-term preservation by leveraging economies of scale.

(KRDS2 J pp.32-34J 79-80)

C>Recommendation toInstitutionsRepositories should take advantage ofeconomies of scaleJ using multi-institutional collaboration andoutsourcing as appropriate. Once corecapacity is in place additional contentcan be added at increasing levels ofefficiency and lower cost. (KRDSIJpp.77-78)

Declining Costs over TilDeWe found a trend of relatively high preservation costs in the early years reducing substantially over time for datacollections. An example is the preservation costs projected for the Archaeology Data Service (ADS) based on theirexperience of the first 10 years of operating the data service. (KRDSIJ pp.4-6)

40 -y--------------

5-Yearly &Cumulative Cost forRefreshment in ADS

Costs for archival storage and preservation (It'refreshment") declineto a minimal level over 20 years

Recommendation to

Funders and InstitutionsThe implications of these factors andprojection for sustainability of dataarchives e.g. via archive charges toproject budgetsJ are notable andworthy of more extensive study andtesting. (KRDSI J pp.5-6)

C>• Cumulative Cost

2010 Years 155

o

:i:2_ Refreshment Cost

..,U)ou

Benefits from digital preservation of research data

Analysis of the costs of preserving research data sets isnot enough to assess economic feasibility. Cost analysisshould be accompanied by a framing of the anticipatedbenefits. As a first step in this process, KRDS has defineda Benefits Framework and a toolkit which includes a listof common generic benefits. Users can sharpen theseshort generic expressions of preservation benefits intomore focused value propositions for specific cases. TheKRDS Framework for categorising the benefits from long-term curation/preservation of research data is presentedto the right. It is illustrated below with examples fromour studies.

Direct BenefitsUnderstanding costs as part of curation saves money. KCLand Southampton currently out-source archival storage tothe Atlas Data Store a central repository maintained by theScience and Technology Facilities Council (STFC).Outsourcing to Atlas has allowed the NCS at Southamptonto reduce costs for archival storage by 41% between whenthis was an in-house and staff-intensive and when this wasoutsourced and highly automated. (KRDS1, pp.70, 74)

Near-Term BenefitsThe constant turnover of post-doctoral researchers oftenresults in lost data. Currently there are no establishedmechanisms to routinely collect and organise the datathat post-doctoral researchers generate. In some cases,researchers that generated data several years ago couldnot make sense of them now as they had not keptenough information on how the data was created. Inthese circumstances, well-curated data has clear shortand medium-term benefits. (KRDS2, p.60)

Internal BenefitsA curated and preserved research data set may generateinternal benefits if the research data set is made publiclyavailable and is frequently used and re-used by externalresearchers, this may increase the visibility and impact ofthe original research, and by extension, enhance thereputation and standing of the researcher and theinstitution in which it was created. (KRDS2, p. 62)

WHO BENEFITS?

Indirect Benefits (e.g. costs avoided)The Digitale Bewaring Project in the Netherlands, whichfocused on government electronic records estimated itcosts approximately 333 euros for the creation of a batchof 1,000 records in an appropriate manner at creation Le.in the Pre-Archive phase. Conversely once 10 years havepassed since creation it may cost 10,000 euros to 'repair' abatch of 1,000 records with badly created metadata.(KRDS1, p.25)

Long-Term BenefitsOne advantage of archiving data over many years is thatlong time series of consistent data are built up. RichardBerthoud has analysed the General Household Surveybetween 1974 and 2005, to describe changing patterns ofadvantage and disadvantage in employment. The analysiswas described by the civil servant responsible forcommissioning the research as having made moredifference to policy thinking than any other project forwhich he had been responsible. (KRDS2, p.72)

External BenefitsExternal benefits may manifest themselves on a variety ofscales: across a group of collaborating universities, acrossthe scientific community as a whole, and even on aneconomy-wide scale, to the extent that long-termpreservation of research data enhances the prospects forcommercialising scientific discoveries, catalysing newcompanies, and expanding opportunities for high-skillemployment. (KRDS2, p.62)

Additional examples of benefits are available in the KRDS Benefits Toolkit and in the KRDS final reports.

Institutional issues: repository modelsOnce the generic benefits and those specific to your institutional goals and ambitions articulated within institutionalstrategies are well understoodJ it is important to define requirements. You can then apply the KRDS cost model tothese requirements to estimate the level of investment needed for the preservation of research data. This will thencontribute to building a business caseJ necessary to release funds or attract investment. When decisions and plansare being made to progress research data preservation initiatives within universitiesJ there are a number of areasthat require careful thought. These will cover both the wider institutional considerations J and the detail of applying acost model to the preservation of research data. One will inevitably influence the other.

• In some disciplines national and occasionally international datarepositories are/will be available.

• There are a number of different service models and structures forresearch data preservation at international J national J and locallevel. There are significant differences and needs betweendisciplines.

• Research data is not as homogenous as research publications andis less likely to be available through a single institutionalrepository.

• Subject knowledgeJ preservation and curation skills are neededfor long-term management of research data.

• The staffing and storage requirements are more substantial forresearch data preservation than for e-print repositories. Annualrecurrent costs for central data repositories are therefore higherthan for e-print repositories.

Repos OrylD s and structures[>

Recommendations toInstitutions

• Consider federated structures forlocal data storage comprising datastores at the departmental leveland additional storage and servicesat the institutional level. Theseshould be mixed with externalshared services or nationalprovision as required.

• Work with and utilise national andinternational disciplinary dataarchives where these exist.

• The hierarchy of data stores shouldreflect the detailed nature of thecontentJ services required J and thechanging nature of its importanceover time.

• Potentially there is considerable scope for economies of scaleacross HEls through either shared services or disciplinary datacentres or centralised repositories at national level.

• Individual researchers are likely to feel alienated if archiving onlyoccurs at an institutional level.

(KRDS1J pp.67-75)

Type of repository eference taff Equipment(capital depreciated

over 3 years)

Institutional Repository SHERPA projec 1 FTE £lJ300 pa(e-publications)

Federated Institutional KCL case study 2.5 FTE £27,546 paRepository (data)

Federated Institutional Cambridge case 4 FTE £58,764 paRepository (data) study

Annual Recurrent Costs: central data repository vs typicalinstitutional repository for e-publications (KRDS1, p.4)

(KRDS1J pp.77-78)

In their own words

It is important to consider theDepartment level in this landscape, inaddition to the overall institutionallevel. It is an academic's naturalaffiliation and an environment theyunderstand and can often have aninfluence on, i.e. it is at this levelwhere money can be raised anddecisions surrounding 'what isimportant' can be made by the mostappropriate people.

(KRDS1, pp.123-124)

Institutional issues: cost variables and data collectionlevels

Key Cost Variables• Institutions may control some of the unpredictability of futurecosts by limiting the future effect of some service variables e.g.the timing of actions has important implications for costs.

• Access costs are potentially the most variable area of costs.Considering some of the access functions as value-added servicescan make it easier to predict long-term costs.

• Evolution of technology and the availability of commercial off theshelf software or mature open source software will havesignificant effect on costs.

• Data collection levels and preservation aims have a major overallinfluence on a number of key cost variables.

(KRDS1/ pp.24-35)

[>Recommendation to Fundersand Institutions

• Implement KRDS in costspreadsheets and continue researchon implementation variables andmetrics that could enhance them.

• Future researchers and theirfunders should note from our workthat longitudinal studies of digitalpreservation costs are bestdeveloped from relatively recentcost evidence.

(KRDS2/ pp.82/ 84)

Data Collection LevelsService requirements for different data collectionsare likely to vary considerably with data havingdifferent value and requirements for access overtime. Significant costs are associated with movingdata collections from one level to another over time.(KRDS1/ pp.59-60/ 164-165)

References

[> Recommendation to InstitutionsConsider utilising the US National Science Board(the governing body for the National ScienceFoundation) long-lived data collection levels(research database collection; resource orcommunity data collection; reference collection)to aid understanding and categorisation of userreauirements and costs over time. (KRDS1.0.77)

KRDS website and KRDS Benefits Analysis Toolkit: http://www.beagrie.com/krds.php

KRDS1: Beagrie/ N./ Chruszcz/ J./ and Lavoie/ B. (2008L Keeping Research Data Safe: a cost model and guidance forUK universities/ Final Report April 2008/ available fromhttp://www.jisc.ac.uk/media/documents/publications/keepingresearchdatasafe0408.pdf

KRDS2: Beagrie/ N./ Lavoie/ B./ and Woollard/ M. (2010L Keeping Research Data Safe 2/ Final Report April 2010/available from http://www.jisc.ac.uk/media/documents/publications/reports/2010/keepingresearchdatasafe2.pdf

Acknowledgements .This factsheet has been prepared by Charles Beagrie ltd and JISC. The Keeping Research Data Safe studies have been funded byJISC and conducted by a partnership of the following institutions: Charles Beagrie ltd, OClC Research, the UK Data Archive, theArchaeology Data Service, the University of London Computer Centre, UKOLN/DCC, and the universities of Cambridge, King'sCollege London, Oxford, Southampton and UCL. For more information see the KRDS website or contact: info@beagrie.com.

Charles Beagrie ltd2 Helena Terrace, College Street, Salisbury SP1 3AN, United KingdomTel: +44 (0)709 2048179, Fax: +44 {O)709 2048179, Email: info@beagrie.com

JISC ExecutiveKing's College London 1st Floor, Brettenham House,S Lancaster Place, London WC2E7EN, United Kingdom

Introduction to the KRDS Benefits Analysis ToolkitOverview

BackgroundOrganisations in the Higher Education sector including the research councils and universities are facing increasingdemands to demonstrate their effectiveness and significant return-on-investment of public funds. This is oftenexpressed in terms of innovation and impact on the UK economy and society but extends to specific investments indigital curation and preservation of research data. Enhancing the ability to demonstrate benefits, value and impactin this context is paramount and this Toolkit is designed to support that requirement.

Development of the Toolkit has been funded by JISC as part of the "KRDSjI2S2 Digital Preservation Benefit AnalysisTools" Project. The project has tested, reviewed and developed further the Keeping Research Data Safe (KRDS)Benefits Framework and the KRDSjl2S2 Value Chain and Benefit Impact Analysis tools for assessing the benefits ofdigital curationjpreservation of research data. It has also extended their utility and wider adoption by providingdetailed user guidance and worked examples for the tools and creating an integrated Toolkit.

The Benefits Analysis ToolkitThe components of the ToolkitTool 1 Tool 2

This leaflet provides an introduction to the Toolkit and itscomponents. The Toolkit consists of two tools: the KRDSBenefits Framework; and the Value-chain and BenefitsImpact tool. Each tool consists of a more detailed guide andworksheet(s). Both tools have drawn on partner casestudies and previous work on benefits and impact for digitalcurationjpreservation. This experience has provided aseries of common examples of generic benefits that areemployed in both tools for users to modify or add to asrequired.

Guide

+

Benefits Worksheet

KRDS Benefits Framework

Guide+

ImpactWorksbeet

+

Value Chain + ImpactWorksheet

Value Chain +Benefits Impact

The Toolkit and its components

How can it be used?

Tool 1

Potential uses for the Toolkit and its component tools

The Toolkit is designed for use by a wide audienceincluding funders, researchers and project staff, andpersonnel in university central services, data archivesand repositories. Common activities which can utiliseoutputs from the different tools in the Toolkit areshown. The KRDS Benefits Framework (Tool 1) is the"entry-level" tool requiring less experience and effortto implement and can be used as a stand-alone toolin many tasks. It can also be the starting point andprovide input to the use of the Value-chain andImpact analysis. The Value-chain and Benefits Impactanalysis (Tool 2) is the most advanced tool in theToolkit and requires more experience and effort toimplement. It is likely to be most useful in a smallersub-set of longer-term and intensive activities suchas evaluation and strategic planning.

fproposals)KRDS BenefitsErallle\VOFI{

Value Chain i+ 'Impact j

r--I ( .ssemination )Si

CompletionReports111:1I

1

Introduction to the KRDS Benefit Analysis ToolkitThe KRDS Benefits Framework Tool

PurposeThe Keeping Research Data Safe (KRDS) Benefits Framework is a tool for identifying, assessing, and communicatingthe benefits from investing resources in the curation/long-term preservation of research data. The Framework isespecially useful as a tool for supporting and organizing early-stage brainstorming on the benefits associated with aparticular activity. The Framework will assist in identifying potential benefits from a project or activity, andarticulating these benefits to a broad audience of stakeholders. It can also aid in customising the expression of thesebenefits to address different stakeholder audiences.

OrganisationEXAMPLE:Benefit: Fulfil research grant datamanagement obligations.

Dimension 1 (Outcome): DirectDimension 2 (When): Near-termDimension 3 (Who Benefits): Internal

Explanation:• The benefit is direct, in the sense that

creating the capacity to fulfil fundingagency requirements to preserve thedata products from research projectsrepresents a distinct, positive sourceof value.

• The benefit is primarily near-term, inthe sense that it permits currentresearchers to fulfil the terms of theirgrant awards.

• The benefit is internal, in that itprimarily extends to those with theappropriate affiliation/authorisationto deposit data into the repository.

The Benefits Framework is summarised graphically in the figurebelow. The Framework organises benefits along three broaddimensions: the outcome achieved; when the outcome isachieved; and who benefits from the outcome. It helps identifythe IIwhat", IIwhen" and IIwho" of the value proposition for theseactivities. Each of these dimensions can be subdivided into twocategories: direct and indirect benefits, near-term and long-termbenefits and internal and external benefits respectively. Thedimensions are applicable to nearly all research datacuration/preservation activities and can be used withoutmodification in most contexts. Any benefit associated with adata curation/preservation activity can be characterisedaccording to these three dimensions.

·WHO BENEFITS?

Anatomy of a benefit

The tool itself consists of a detailed guide and a worksheet. Application of the Framework to a range of projects hasrevealed a number of common benefits that frequently arise from the curation/preservation of research data. A listof examples of these IIgeneric" benefits is provided in the Tool as a starting point for applying the Framework.

2

Introduction to the KRDS Benefits Analysis Toolki'tThe Value Chain and Benefits Impact Tool

PurposeThis is the second and most advanced tool in the Toolkit. Once benefits are identified and organised within theBenefits Framework, further work can proceed aimed at identifying potential measures or illustrations of the valueand impact of those benefits. This next stage is supported by the Value-Chain and Benefits Impact Tool. This can beused in assessing where value is added to outputs in a chain of activities and for use in evaluation, strategic andorganisational planning, and reporting.

The Tool is intended help you to identify quantitative metrics and qualitative indicators for the impact of benefitsand optionally to support a value-chain analysis. It uses the KRDS Activity Model as a starting point for the value-chain analysis, so it is better suited to the specific needs of research data and its curation/preservation.

OrganisationA detailed user guide and two worksheets have been provided with the Tool; the Benefits Impact worksheet and theValue-chain and Benefits Impact worksheet. An extract from the later is shown below. To use this Tool, you shouldfirst select which worksheet most closely matches your needs. Both worksheets have been pre-populated with aselection of common generic benefits also used in the Benefits Framework Tool but you may review, delete or addmore to the selection. The tool has been designed to be generic but easily configurable by the user for their specificneeds or application.

Extract from KRDS Value-chain and Benefits ImpactWorksheet

We recommend that both worksheets in the Tool are used by a team with a senior member of staff or independentsupport (e.g. consultancy). For maximum effectiveness in applying the Tool, ideally at least one person in the teamshould be very familiar with the KRDS Benefits Framework (tool 1), other KRDS Outputs such as the KRDS ActivityModel, and similar assessments of value and impact.

Demonstrating the impact of benefits for research data curation/preservation, either directly via metrics(quantitative impacts) or qualitatively via illustration in case studies (qualitative impacts), is still a relatively novelarea. The guide provides discussion and further references to JISC and Research Councils' work on demonstratingimpact. This can provide examples to assist working though how to demonstrate the impact of benefits andimplement capturing the relevant measures/illustrations identified in completing the worksheets.

3

Introduction to the KRDS Benefits Analysis ToolkitThe Combined Toolkit

Combining use of the ToolsThe starting point for projects and organisations using the Toolkit will always be the Benefits Framework (Tool 1).Once benefits are identified and organised within the Benefits Framework, further work can proceed aimed atidentifying potential measures or illustrations of the value of those benefits. Outcomes from using the BenefitsFramework can be re-used in doing this. If desired you can also consider these benefits and impacts in a value-chainanalysis. These stages are supported by worksheets in the Value-Chain and Benefit Impact Analysis (Tool 2).

There are several fields in common between the twotools. Each of the benefit dimensions in the BenefitsFramework Worksheet (Tool 1) is represented byseparate columns in the worksheets for the Value Chainand Benefits Impact (Tool 2) so it is possible to build onand re-use your previous work within the Toolkit asneeded.

The combined Toolkit provides a very flexible set of tools,worksheets, and lists of examples of generic benefits andpotential metrics. These are available for you to use indifferent combinations appropriate to your needs andlevel of expertise. Guides for each tool and case studiesof completed examples of the worksheets (see theBenefits Analysis Tools Project web site) providedocumentation and support for your implementation.

The Toolkit is available to download from the project website and the KRDS web site (details below).

References

Tool 1Dimension 1:What are!he outcomes?

Indire,ct B nefits ,(e.g. costs avoi ad)

(insert here]

Increasing researchproductivity I

No loss offuture researchopportunities

Input for future research-'Motivating new researchStimulating new networks i I i=== ;

and collaborations L.------ . INew research 1 - I -- -:--,--'------'--j---opportunities: . ! I I. I

ShorHerm re·use,ofwelt---l----r-- r-!----1---.1,---··__..,...,_.- --+-..,-,-----.. --_., ...,!-_.,.-." ... ,., ..,... ", .....",...+....,.".".

Benefit dimensions as they appear in Tool 1 & Tool 2

KRDS/12S2 Digital Preservation Benefit Analysis Tools Project web site: http://beagrie.com/krds-i2s2.php

KRDS web site: http://www.beagrie.com/krds.php

JISC Research Data Management Infrastructure (RDMI) projects benefits case studies:http://www.jisc.ac.uk/whatwedo/programmes/mrd/outputs/benefit studies

AcknowledgementsThis Introduction leaflet has been prepared by Charles Beagrie ltd and the KRDS/12S2 Digital Preservation BenefitAnalysis Tools Project partners. The project has been funded by JISC and conducted by a partnership of the followinginstitutions: UKOlN and the Digital Curation Centre at the University of Bath, the Centre for Health Informatics andMulti-professional Education (CHIME) at University College london, the UK Data Archive (University of Essex), theArchaeology Data Service (University of York), OClC Research, and Charles Beagrie limited. For more informationsee the project web site or contact: info@beagrie.com. Copyright Charles Beagrie limited 2011 and KRDS/12S2Benefits Project Partners 2011.

4

Institutional ROMpolICy

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Outcomes

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Figure 1. Illustration showing overview of in'diumproject's approach and associated worl<packages.

evaluation User support

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Output to DCC IProgramme :WP!l:

Figure 3b. Diagram of the iridIUm project's policy space concept.

Figure 2. Current project topics have been recorded on an evolving mind map.

Figure 3a. Diagr am of the iridiumproject's policy layering concept.

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