©keith g jeffery/ anne assersonsupporting the research process with a cris cris2006 1 supporting...

©Keith G Jeffery/Anne Asserson Supporting the Research Process with a CRIS

CRIS2006 1

Supporting the Research Process

with a CRISKeith G Jeffery Director IT CLRC [email protected] President, euroCRIS

Anne Asserson Senior ExecutiveOfficer [email protected] University of Bergen


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Agenda

• The Issue• The Proposition• The Research Process• Dealing With the Issue• The Metadata• Conclusion


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The Issue

• Increasing numbers of researchers• Increasing output per researcher

– Publications– Patents– Products

• Especially research datasets from automated equipment

• Effort to catalog - input metadata– Too great (for the user)– Does not scale (with increasing numbers)


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Agenda



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The Proposition

• The research process provides the context– Link the CERIF-CRIS information to the research

output information• Provides context• Provides some of the required metadata

– Collect metadata fragments • Only once• As early as possible (as they are generated)

• Result– Research output

• Publications, patents, products– Linked together in context by the CERIF-CRIS

• Person, Project, OrgUnit, Funding, Event, Facility, Equipment

– With provenance and curation managed automatically


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The Notion

• The research process is a workflow with e-forms– At each step (meta) information is required

and stored incrementally (re-use, minimal effort)

• The researcher sees benefit from the process: examples– Automated CV– Automated publication list– Tracking competing and cooperating teams– Research visible to intermediaries for

exploitation– Boilerplate information for research proposals


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Agenda



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The R&D Process: Recording

Workprogramme

Proposal

Project

Results

Exploitation

WealthCreation

CRISDATABASE

Information from external systems and CRIS


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The R&D ProcessRecording WorkProgramme

Workprogramme ProgrammeNameFundingOrgUnitPerson

Workprogramme document

CRISDATABASE


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The R&D ProcessRecording Proposal

Proposal

TitleAbstract

Person(s)OrgUnit(s)

Proposal Document

CRISDATABASE


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The R&D ProcessRecording Project

Project

TitleAbstract

Person(s)OrgUnit(s)

FundingProject Plan

CRISDATABASE


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The R&D ProcessRecording Results-Product

Results

Person(s)OrgUnit(s)Project(s)

Product(s)Product Description

CRISDATABASE


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The R&D ProcessRecording Results-Patent

Results

Person(s)OrgUnit(s)Project(s)Patent(s)

Patent File

CRISDATABASE


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The R&D ProcessRecording Results-Publication

Results

Person(s)OrgUnit(s)Project(s)

Bibliographic InformationArticle

CRISDATABASE


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The R&D ProcessRecording Exploitation

Exploitation

Person(s)OrgUnit(s)

Business planFinance Data

Marketing DataProduction Data

Sales Data

CRISDATABASE


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The R&D ProcessRecording Wealth Creation

WealthCreation

Person(s)OrgUnit(s)

Annual Reports/AccountsEmployment Records

Dividends Records

CRISDATABASE


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The R&D Process

Workprogramme

Proposal

Project

Results

Exploitation

WealthCreation

Note:

some CRIS developers limit recording of outputs from the process to areas indicated

Nirvana


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CRIS Features Required

• Entity instance attribute data collected once and stored

• Entity instances related flexibly (n:m)• Entity instances related by role and temporal

limits (semantics)• Input incremental, flexible, validated (minimum

effort)• System extensible (add new attributes,entities

preserving previous datastructure for interoperation)

• System interoperable – CRIS (to create world view)

• System linkable – other systems used in research process (eg finance, HR, project management to utilise them for CRIS purposes)


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CERIF-CRIS

• It is no accident that CERIF (Common European Research Information Format) provides a datamodel with exactly these desirable properties.

• Linking relations are the key feature – temporal and role information

• Critical to answer questions like:– “during what time interval was person A

project leader of project P?” – “to which research group(s) did person A

belong when she produced publication X?”


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CERIF-CRISFurther features

• Inference: – in a multidimensional framework, – deduction or induction of relationships between

entities• eg between a grey internal report and a white published

paper - and with other research outputs such as datasets or software.

• Fact generation– automated generation of facts

• eg (1) Person A on Project P produces Paper X;• (2) Project P uses Equipment E• Person A uses Equipment E

– the generated data may be • recorded in the CERIF-CRIS • deduced / induced afresh each time it is required.


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CERIF-CRISFurther features

• Assertions– relationships between entity instances (eg documents)

can also be expressed explicitly (i.e. asserted)• eg references and / or citations can be recorded by

directly inputting the information into the CERIF-CRIS.

• Metrics– role-based temporal relationships between entity

instances (eg publications)– provides detailed research output metrics, – increasingly in demand from CRISs as research

institutions seek to justify their funding and to improve their relative standing in league tables

– while funding organisations seek to justify their decisions.


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CERIF-CRISSummary

• through the flexible and dynamic linking relations between entities, – with their role and time-stamped attributes,

• a rich context for understanding the R&D output is provided, including versions, history and provenance.

• This context is particularly important for other users of CRISs such as – entrepreneurs engaged in technology transfer and

wealth creation – the media explaining to the public the importance of

the research being done.


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CERIF-CRIS at the Centre

• Acting as metadata• Relating CRIS information to itself

– Flexible linking relations

• And to information in other systems– Eg publications repository– Eg e-research datasets and software

• And Via GRIDs environment to other research process systems– E.g. finance, HR, project management


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CERIF-CRIS at the Centre

Portal with knowledge-assisted user interface

Digital Curation Facility

SCIENTIFIC DATASETS

Data

Information

Knowledge

PUBLICATIONS

Data

Information

Knowledge metadata

publish

validate

GRIDs

Ambient, Pervasive Access


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Agenda



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Dealing with the Issue: Progressive

Recording• early research ideas or work in

progress : grey document – described by appropriate metadata

(title, abstract….) input at the time of deposit.

– publication metadata linked to pre-existing research information (such as person, organisational unit, project) in a temporal and role-based context.


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Progressive Recording

Grey DocumentGreydoc

Publicationmetadata

Person

Project

OrgUnit

new


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Dealing with the Issue: Progressive

Recording• early research ideas or work in progress :

grey document – described by appropriate metadata (title,

abstract….) input at the time of deposit. – publication metadata linked to pre-existing research

information (such as person, organisational unit, project) in a temporal and role-based context.

• grey document developed into a white publication– additional publication metadata is input at the time

of submission. – linked through temporal and role-based

relationships to the pre-existing grey publication – and to the pre-existing contextual information such

as persons, organisational units etc.


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Progressive Recording

White documentGreydoc

Publicationmetadata

Person

Project

OrgUnit

Whitedoc

Publicationmetadata

new


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Dealing with the Issue: Re-Use for

Scalability• Record (meta)data once: re-use

many times• Record only the metadata available

and needed at each process step– Automated input assistance - quality– Reduces input required

• Addresses scalability and high user effort threshold, improves quality


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Agenda



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MetadataWhere to Store it

• In the repository (publications or e-research datasets, software)

• In the CERIF-CRIS


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Metadata in the Repository

• Advantages– Metadata with the object

• Available for retrieval, statistical processing, advanced computation…

• Available for harvesting (eg OAI-PMH)

• Disadvantages– Metadata not available in CERIF-CRIS for

management information– Most repositories only store poor metadata

• non-machine-understandable• Insufficient for bibliographic reference• No DOI to link to publisher database


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Metadata in the CERIF-CRIS

• Advantages– Efficient processing of management

information queries • Disadvantages

– Have to somehow redirect OAI-PMH harvesting to CERIF-CRIS instead of repository

– Separate metadata from the full hypermedia article, research dataset or software


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The Solution: Metadata in CERIF-CRIS and

Repository• Primary metadata source is in the

CERIF-CRIS– Linked with research process workflow– Incremented as generated– Provenance and context– Validation – quality– Generate bibliographic references

• Copy in the repository– For harvesting (articles)– With additional detailed metadata for

research datasets or software


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The Solution: Metadata in CERIF-CRIS and

Repository• Discussion

– Parts of (meta)data stored twice, but • storage is cheap• Research process workflow means only input

once

– Improved quality through validation due to context and provenance

– Management Information processing performed in one system and separated from access to the research articles, datasets or software


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Agenda



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Conclusion

• The solution presented works in prototype designs:– UiB: FRIDA (CERIF-CRIS) linked to

DSpace– CCLRC: CDR (CERIF-CRIS) linked to

ePubs (articles) and e-Research portal (datasets and software)

• And is now being implemented in production

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