Roberto C. S. PachecoEGC/UFSC INE/UFSC Instituto Stela - Brasil

Toward CERIF-ScienTI Cooperation and Interoperability

The ScienTI and CERIF Models: a Compatibility Analysis Towards Interoperability Among European and Latin-American ST&I

Information Networks

euroCRIS 2006Bergen, Norway - May 12th 2006

SAS Hotel Bryggen. Lecture Hall

Vinicius Medina Kern; José Salm Jr.Instituto Stela - Brasil

Abel Laerte Packer; Renato MurasakiBIREME/PAHO - Brasil

Luis Amaral; Leonel Duarte dos SantosUniversidade do Minho - Portugal

Alberto Cabezas BurlemoreCONICYT - Chile

• Introduction– Mapping information on a NIS

• ScienTI Approach

• ScienTI and CERIF – Different world views– Complementariness– Cooperation in Perspective

TOPICS

Introduction – A National Innovation SystemMacroeconomic andregulatory context

Education andtraining system

Clu

ster

s of

indu

strie

s

Globalinnovation networks

Reg

iona

lin

nova

tion

syst

ems

National innovationsystem

Communicationinfrastructures

Factor marketconditions

Product marketconditions

COUNTRY PERFORMANCEGrowth, jobs, competitiveness

National innovation capacity

Knowledge generation, diffusion & use

Supportinginstitutions

Sciencesystem

Otherresearchbodies

Firm’scapabilities& networks

University Universi

ty

GovernmentGovernm

ent

Industr

y

Industry

NIS – National Innovation System ModelFreeman, 1987. Lundvall, 1992 OECD, 1999.

Triple Helix ModelEtzkowitz & Leydesdorff, 2002.

• CRIS have impact on all players and sources that are relevant to ST&I stakeholders– Government– S&T Community – Universities and Research Institutes

– Industry– Economy – Legislation

– Intellectual Property– Commerce– Etc…

NIS main features regarding information management

• Highly decentralized processes with– Players with different timetables and requirements– Several world views of how S&T information should be

mapped

Please, would you be kind enough to ask all authors their official national IDs?

Sorry but our system does not know for sure whether the funded authors are the same as article authors?

Librarian

Funding agent

Then I know for sure that I cannot pay them !!!

No way!

NIS main features regarding information management

• The inevitable consequences:– Proliferation of data models, information projects and

data sources• Multiple Funding Agency systems;• Multiple Other Public Systems (in each correspondent Ministry) • Multiple R&D organizational systems; • Multiple Librarian Systems; • Multiple Educational Systems; • Multiple Firm Systems;• Multiple Sector Portals;

– Although there is a conceptual NIS there are several ST&I information flows.

1. How one can foster S&T information sharing by combining workflow and information modeling?

• By establishing standards that support all processes in the innovation chain

• By adopting IT architectures that establish methodological and technological frameworks for future developments

Research Challenges Some of the Answers

2. How such approach can result from the cooperation involving all ST&I players?

• Involving the users • Involving different CRIS designers• Practical Virtual Communities

– CRIS designers - propose and maintain the standards (national and internationally compatible)

– User communities - tell us what should be done in the next CRIS versions.

Research Challenges Some of the Answers

3. How government, universities, R&D organizations, firms or other information owners should develop their CRIS?

• Respecting the standards (specially governmental authorities)

• Following best practices (IT architecture, openness, interoperability and information sharing)

• Having a plan for involving, communicating and motivating the users

Research Challenges Some of the Answers

ScienTI Approach

Reference International standards

Methodology

eGov Architecture

International Network

ScienTI systems

ScienTI standards

Web services models

ScienTI NetworkScienTI NetworkInternational Network on Information Sources and Knowledge International Network on Information Sources and Knowledge for the Management of Science, Technology and Innovationfor the Management of Science, Technology and Innovation

ONCYTs OICYTs GDIs

2000

VHL Science and Health Meeting

• MeetingPAHO/BIREME, CNPq and ONCyTs from Latin America and the Caribbean coutries

• Agreement: links between SciELO-CvLattes

• Perspectives: to create a health information sources network in Latin America and the Caribbean

2001

CVLACS System

• V CRICS: Presentation of beta version to ONCyTs

• Piloto: Chile, Colombia, Cuba, Mexico and Venezuela

• Portugal: integrated to the project DeGois Pilot

1998/1999

Genesis

• VHL SH: PAHO/BIREME.Information and knowledge sources in health

• Lattes Platform: CNPq/MCT: Integrated management of Science, Technology and Innovation

ScienTI Timeline

2002

ScienTI Network

• CvLAC System in Colombia• GrupLAC System in Colombia• Bilateral agreements

7 countries with CNPq.• Formalization: I Florianópolis

Meeting, December 2002• Organization:

Proposal in Agreement and Inter-institutional Committee.

2003• Institutionalization

II ScienTI Meeting. Puebla (VI CRICS)

• Executive Secretariat – BIREME

• Indicators. RICYT and PAHO’s studies of the network use in the production of indicators

Consolidation

2004• Launching in other

Countries: • Peru• Venezuela

• Technology and Methodology. Adoption of web services to descentralized model

• III ScienTI Meeting – Buenos Aires

Extension

ScienTI Timeline

2005

Formalization

• Workshop about ScienTI Web Services:

6 countries participated• Debate for the signature in the

Agreement of Cooperation 12 countries participated.

• Technology and Methodology. Practice communities available in the ScienTI Regional Portal (documents, chats y foros)

• Network structure GDI network defined

• IV ScienTI Meeting – Salvador, Brasil

ScienTI Timeline

2006• Brasil: Innovation Portal, Increasing of Institutional solutions

and other public thematic approaches (health survailance, education, environment, etc)

• Colombia: Peer review system, Firms module, job opportunities

• Chile: SICTI Portal, Event agenda, New Business model for ScienTI as sustainable space for web services

• Peru: National Portal searching for CVs• Portugal: ScienTI research advancements and planning for

help national R&D funding• PAHO: International Expertise locator based on ScienTI

model and connectable to institutional or national databases• Japan – CNPq presents Lattes Platform in Japan beggining

cooperation in nanotechnology, biotechnology and biomasshttp://www.jornaldaciencia.org.br/Detalhe.jsp?id=37535

Nationally spreading

PROJECTPHASE

Requirements(considering all users)

Planning(CRIS life cycle)

RelatedProjects Studies

FormingCommunities for

Standards

Development andDeployment

Management andMaintenance

Creating andManaging

Knowledge

Services(e-services)

Interactingwith Users

VirtualCommunities

InformationSources

OPERATIONPHASE

MethodologyPacheco, 2003

• METHODOLOGY: Perceiving design, development, use and revision as a continuous process is the key to make national platforms constantly growing.

eGov Architecture

CvDeGois

CvLattes

CvLAC

DM Cv DM Gr DM Inst DM Proj

Investiments Indicators

Analysis Systems

Searches

CVs Groups Institutions Projects

SCienTI Directories

ScienTI Portals

Sistema Grupo

GrupLAC

CV

s

ResearchersStudentsProfesionals

CvDeGois

CvLattes

CvLAC

Research Group Leaders

Sistema Grupo

Sistema GrupLAC

Investimentos em CT&IScienTI System - Players

Managers in ST&I

InvestmentsCT&I

Universities

Link Analysis

Former Students

ST&I Organizations

Cv Viewer

Gro

ups

ScienTI Network Information Flow

• What they are not– Technological platforms

So in order to be in ScienTI the same technology is needed to every one

– Single “Big Machine” ApproachesAs mentioned yesterday by Stefan Gradman

– Silver bullets

Some Remarks on the Methodology and Architecture

• What they are– Methodological and Architectural

ReferencesThey offer to ScienTI members reference models to concept and build CRIS

– Replicable ApproachesThey can be applied at organizational, funding agency or international networking levels

– Long Run Support for NIS modelsThey allow the construction of different CRIS views by keeping the perspective of model and best practices references

Some Remarks on the Methodology and Architecture

Did we attend most of Did we attend most of stakeholders stakeholders

information needs?information needs?

ScienTI Brazil – Indicators

1,8 million access a year

19.140 research groups in more

than 200 institutions

+713.000100 thousand CVs per year

COUNTRY.……………….2002…….2005Argentina.................................0……35.580Brasil............................248.000…..590.000Colombia........................13.500……40.000Chile.................................2.000……..7.290Equador...................................0……….430Mexico.....................................0……11.900Peru.........................................0……..2.200Portugal...........................…….0…..…4.100Venezuela……………………...0……..1.420TOTAL…………………263.500……692.920

ScienTI CV Numbers (September 2005)

Example Using Dynamic Data Marts to yield Indicators for S&T Management

SpecializedSearches

DM CV

Curricula

Server OLTP CV DatabasePrimary Information Source

Cv System

Curriculum Data MartSecondary Information Source

POLI

CY

MA

KER

S

Supporting the National Policy on Industry Development Strategic Areas (Brazilian PITCE)

Semicondutores

1238 pessoas

c/ registros

157 inventores

Distr ibuição de patentes de semicondutores

0,00

2,00

4,00

6,00

8,00

10,00

12,00

14,00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Pesquisadores

% d

o to

tal d

e pa

tent

es

distribuição de registros

Fármacos

2629 pessoas

c/ registros

438 inventores

Distr ibui ção de pate ntes de fár m aco s

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Pesq uisadore s

% d

o to

tal d

e p

aten

tes

Semiconductors• 1.238 people working in the area• 157 (12,6%) have IP• Only 2 researchers have 18% of all IP

Pharmacy• 2.269 people working in the area• 438 (16,7%) have IP• 15% have 50% from the total IP in the area

Social NetworkAnalysis

DM Group

Groups

Primary InformationSources

Cv System

Group and Curriculum Data Marts

Curricula

DM CV

GroupSystems

Dynamic Data martsSocial Network AnalysisEV

ALU

ATO

RS

ScienTI Networking Analysis: Some Results (Brazil)

Social Network Analysis(e.g., searching for hubs on a national list of experts)

- A search on a certain area shown that there are only 3 hubs on a list of the 40 researchers with the majority of P&T registers on their CV

- The three hubs belong to the same graduate program

Balancieri, R. 2004.

Automatic Semantic AnalysisK

NO

WLE

DG

E R

ESEA

RC

HER

S

DM CV

Curricula

Server OLTP CV DatabasePrimary Information Source

Cv System

Curriculum Data MartSecondary Information Source

Analysis Systems

Hidden Relations for “Knowledge”

Lattes Platform InstitutionalizationU

NIV

ERSI

TIES

AN

D R

&D

INST

ITU

TIO

NS

Organizational research policiesResearch evaluationResearcher carrier plan

Finding Experts...

Firm’sEnvironment

How to make the national competences available to firms for contact and collaboration?

www.portalinovacao.info

Search for Competences

Search for Competences

ANALYSIS OF THE EXPERT’S PROFILE

Search for Competences

Search for Competences

SAVING EXPERTS IN “FAVORITES” FOR FUTURE COLABORATION

Search for Competences

Prezado Pesquisador,

Estivemos consultando o Portal Inovação e verificamos sua experiência em temas de nosso interesse para uma possível cooperação visando capacitação. Assim, gostaríamos de verificar a possibilidade de realizarmos uma reunião

Saving in “Favorites” and Making Contact...

Back to Back to CERIF-ScienTICERIF-ScienTIpossibilities…possibilities…

Standards and InteroperabilityStandard Community of Standard Community of practicepractice2000-20042000-2004

Cooperation• Each ST&I unit of analysis

should be defined as a result of collaboration between the different players

• The standards have to be constantly updated to attend new trends and needs

• Interoperability has to be part of the agenda

http://lmpl.cnpq.br/lmpl/

CV Standard

CV standard includes subunits (e.g. bibliographic production) and a XML schema to control format and obligatory fields

Identification, Professional address, Academic degree, Knowledge fields; research projects; Professional experiences, idioms; awards

Articles; Conference papers; Books and chapters; newspaper articles; other kind of publications

Software; products; processes; technical reports; intellectual property; other kind of technical work;

Artistic work; PhD/master/undergraduate thesis; other kind of work

Short courses; PhD/master/undergrate Committees; peer reviews; participation in event; etc.

Research Group Standard

Research group standard includes subunits (researcher, students) – by identification fields and also XML schema to control format and obligatory fields

Institution; Address; Research leaders; Work repercussion.

Identification; research lines; students

Identification; advisor; research line

Identification; academic degree, work in the group

Research areas; keywords; people involved

Group partners; kind of partnership; kind of sponsorship

Organization Standards

Examples

Institution standard has a three layer conceptual design: (a) General Model describes modules such as identification, historic,

kind of structure, etc.

(b) Expanded Model describes codification, classification, and other fields with reference domains (e.g., job classifications); and

(c) Client system Model – describes specific fields needed in particular applications

GENERAL MODEL – BASIC DATA: Mission; historic; foundation date

EXPANDED MODEL: Organization structure

GENERAL MODEL: Legal authority

EXPANDED MODEL: Organization code (kind; entity definer; code; and code description

• CERIFOrgUnit, Person and Project

• ScienTIInstitution, Curricula and Project

ScienTI and CERIF Information Basic Entities

Entities – Shared View CERIF and ScienTI

• AcademicTitle• Contact• Country• CV• ExpertiseAndSkill• ExpertiseAndSkillDescription

• ExpertiseAndSkillName• HonorificTitle• Language

• OrgUnit• OrgUnit_Contact• OrgUnit_ExpertiseAndSkill

• OrgUnit_OrgUnit• OrgUnitName• Person_AcademicTitle• Person_Contact• Person_CV• Person_ExpertiseAndSkill• Person_Language

Fields present in both CERIF AND ScienTI models

• Project_Person• Project_ResultPatent• Project_ResultProduct• Project_ResultPublication

• ProjectAbstract• ProjectKeywords• ProjectTitle• ProjectStatus

• Person_Nationality• Person_OrgUnit• Person_Person• Person_ResultProduct• Person_ResultPublication

• PersonResearchInterest• Project_Classification• Project_FundingProgramme

• Project_OrgUnit

Entities – Shared View CERIF and ScienTI

Fields present in both CERIF AND ScienTI models

• Different World Views– Information modeling drivers

• ScienTI models (and architecture) were driven by the need of gathering national information for funding management

• CERIF model was driven as a reference model (recommendation) without specific CRIS project

CERIF And ScienTI Models Differences

– Information redundancy approaches• CERIF is a conceptual model based on normalized non redundant data

• In ScienTI the assumption of gathering information as close as possible to the owner and as a distributed system

– Then the model is composed by units with subunits (such as publication within CVs) and individual fulfilling (such as authors keywords that lead individual vectors different from the article vector)

– Redundancy is treated by capturing and treating 3 information formats: » Relational + XML at the researcher desktop; » Relational and multidimensional at the server

• Areas of cooperation– ScienTI National Translations

• CERIF flexible classification schemas

• CERIF idiom translation approach

– CERIF concerns about incentives• ScienTI systems were designed to gather information but also to offer

services to the users (personal cv-website; legacy import systems; knowledge-based profiles, etc.).

• These incentives have been proven even when facing government policy changes

– starting in 2003 the new Brazilian CNPq authorities disagree on the initial Lattes assumption that new knowledge can be discovered from funding information)

CERIF And ScienTI Models Opportunities

• Regarding Interoperability– Both CERIF and ScienTI models have been proved as

open models that can foster connectivity, information sharing, and compatibility in CRIS.

• Mutual Strengths– CERIF has a mature and normalized model that eliminates

redundancy and may play the role of design pattern for future ScienTI developments (particularly at the server level);

– ScienTI has reached a wide public and uses strategies that proved to successful to make the database grow and remain constantly updated

Some Conclusions

• Future– A collaboration between CERIF and ScienTI

could bring significant contributions to both projects an to all players in the correspondent NIS;

– Portugal is a participant of both initiatives and may contribute to accelerate such collaboration;

– Most importantly European and Latin American NIS would benefit from information sharing and cooperation

Some Conclusions

48

Possible FuturePossible Future CERIF and Scienti info projectsCERIF and Scienti info projects

Roberto C. S. PachecoEGC/UFSC INE/UFSC Instituto Stela - Brasil

Toward CERIF-ScienTI Cooperation and Interoperability

The ScienTI and CERIF Models: a Compatibility Analysis Towards Interoperability Among European and Latin-American ST&I Information Networks

euroCRIS 2006Bergen, Norway - May 12th 2006

SAS Hotel Bryggen. Lecture Hall

Vinicius Medina Kern; José Salm Jr.Instituto Stela - Brasil

Abel Laerte Packer; Renato MurasakiBIREME/PAHO - Brasil

Luis Amaral; Leonel Duarte dos SantosUniversidade do Minho - Portugal

Alberto Cabezas BurlemoreCONICYT - Chile

pacheco@egc.ufsc.br

pacheco@stela.org.br

Thank you!! MUITO OBRIGADO!