Presentation about new indicators for innovation missions focusing on the mission to transform the prevention, diagnosis and treatment of AI, given at the EMAEE conference, University of Sussex 5 June 2019.
Experiment of the Assessment of Societal and Economic Impacts by Policy Simul...scirexcenter
Masahiro Kuroda, Kenta Ikeuchi, Yasushi Hara, Michel C. Huang.
National Graduate Institute for Policy Studies (GRIPS), Japan
Kazuyuki Tsuchiya, Akira Ohtagaki
Mitsubishi Research Institute
Masatoshi Yokohashi, Kaori Tsuyuki
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This presentation was provided by Holly Falk-Krzesinski of Elsevier during the NISO event, "Is This Still Working? Incentives to Publish, Metrics, and New Reward Systems," held on February 20, 2019.
This document discusses measuring and capitalizing knowledge assets from R&D investments. It presents several ways to link R&D spending to knowledge creation and economic outputs:
1) Tracking patent applications and publications to measure knowledge outputs from R&D.
2) Surveying firms about innovation activities to understand R&D's link to new products and processes.
3) Estimating total factor productivity and GDP growth both with and without accounting for intangible capital like R&D to show its contribution to economic growth.
The document advocates improving national accounts to fully capture R&D's economic impacts through linking various data sources on patents, publications, surveys and financial data. This will provide a more comprehensive
Presentation about the state of AI, policy-relevant AI research and evidence gaps that can be addressed with new data, methods and modelling approaches.
Nagaoka - Comments on Science and Innovation policy making todayinnovationoecd
1. Scientific knowledge enhances technological innovation but the flow of knowledge and its impact are poorly measured. Incomplete metrics may misguide policymaking.
2. Surveys of scientists and inventors can help understand limitations of existing indicators and provide complementary information, such as what top citations indicate, the role of information cascades, and how significantly inventors utilize scientific knowledge.
3. Collaborative mechanisms like standards setting are important for coordinating R&D and diffusion but their relationship to innovation is not well understood due to lack of data linking standards to patents. Better data collection is needed to measure innovation processes and impacts.
Borner - Modelling science technology and innovationinnovationoecd
Modeling Science, Technology, and Innovation
This document discusses modeling science, technology, and innovation (STI) using qualitative and quantitative data. STI models are developed in various domains to describe and predict the structure and dynamics of STI. Models help make assumptions explicit, describe systems, communicate systems, suggest interventions, and identify new questions. The document outlines opportunities for using big data, visual analytics, and computational models in STI decision making. It also announces a forthcoming special issue of Scientometrics on simulating STI processes and describes previous exhibits and forecasts related to modeling STI.
Presentation about new indicators for innovation missions focusing on the mission to transform the prevention, diagnosis and treatment of AI, given at the EMAEE conference, University of Sussex 5 June 2019.
Experiment of the Assessment of Societal and Economic Impacts by Policy Simul...scirexcenter
Masahiro Kuroda, Kenta Ikeuchi, Yasushi Hara, Michel C. Huang.
National Graduate Institute for Policy Studies (GRIPS), Japan
Kazuyuki Tsuchiya, Akira Ohtagaki
Mitsubishi Research Institute
Masatoshi Yokohashi, Kaori Tsuyuki
Applied Research Institute
This presentation was provided by Holly Falk-Krzesinski of Elsevier during the NISO event, "Is This Still Working? Incentives to Publish, Metrics, and New Reward Systems," held on February 20, 2019.
This document discusses measuring and capitalizing knowledge assets from R&D investments. It presents several ways to link R&D spending to knowledge creation and economic outputs:
1) Tracking patent applications and publications to measure knowledge outputs from R&D.
2) Surveying firms about innovation activities to understand R&D's link to new products and processes.
3) Estimating total factor productivity and GDP growth both with and without accounting for intangible capital like R&D to show its contribution to economic growth.
The document advocates improving national accounts to fully capture R&D's economic impacts through linking various data sources on patents, publications, surveys and financial data. This will provide a more comprehensive
Presentation about the state of AI, policy-relevant AI research and evidence gaps that can be addressed with new data, methods and modelling approaches.
Nagaoka - Comments on Science and Innovation policy making todayinnovationoecd
1. Scientific knowledge enhances technological innovation but the flow of knowledge and its impact are poorly measured. Incomplete metrics may misguide policymaking.
2. Surveys of scientists and inventors can help understand limitations of existing indicators and provide complementary information, such as what top citations indicate, the role of information cascades, and how significantly inventors utilize scientific knowledge.
3. Collaborative mechanisms like standards setting are important for coordinating R&D and diffusion but their relationship to innovation is not well understood due to lack of data linking standards to patents. Better data collection is needed to measure innovation processes and impacts.
Borner - Modelling science technology and innovationinnovationoecd
Modeling Science, Technology, and Innovation
This document discusses modeling science, technology, and innovation (STI) using qualitative and quantitative data. STI models are developed in various domains to describe and predict the structure and dynamics of STI. Models help make assumptions explicit, describe systems, communicate systems, suggest interventions, and identify new questions. The document outlines opportunities for using big data, visual analytics, and computational models in STI decision making. It also announces a forthcoming special issue of Scientometrics on simulating STI processes and describes previous exhibits and forecasts related to modeling STI.
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• Invited speaker, Manchester Institute of Innovation Research, Manchester Business School, The University of Manchester, Manchester, United Kingdom, 17 February, 2009
The document discusses the importance of "education-supporting research" (ESR) for developing Norway's innovation capability in information and communication technology (ICT). It defines ESR as research that supports and strengthens higher education quality, recruitment, relevance, and strategic development. The document argues that ESR has been crucial for establishing many of Norway's most important ICT innovation successes. It recommends that future public ICT research funding place a strong emphasis on ESR to sustain Norway's global position in ICT innovation and ensure sufficient innovation capability. The document also recommends setting targets to increase the number of PhD students in ICT and ensure a significant portion of their education is integrated into research projects.
The document discusses the historical evolution of priority setting in science, technology, and innovation (STI) policy. It describes four major paradigms: 1) a mission-oriented approach from the postwar period focused on large-scale technologies; 2) an industrial policy approach emphasizing strategic technologies; 3) a systemic approach focused on the innovation system; and 4) a new mission-oriented approach engaging stakeholders on societal challenges. It analyzes trends toward more actors, decentralization, explicit strategies, and focus on broad missions and the innovation system. Challenges include establishing sound rationales, avoiding lock-in, ensuring coherence, and using strategic policy tools like foresight.
Rafols - Towards more inclusive STI indicatorsinnovationoecd
This document discusses the need for more inclusive science, technology, and innovation (STI) indicators that better capture diverse types of research and innovation.
Current STI indicators are biased towards certain types of mainstream science and may suppress or exclude valuable creative research in other fields like agriculture. This can threaten diversity in research. Indicators are also needed that make other contributions visible, like action research or co-creation.
While STI indicators can help with decisions, they do not necessarily lead to the "right" decisions if they do not reflect the full range of social and economic functions of science. Expanding indicator data and developing new indicator types may help broaden coverage of societal problems and peripheral areas of research.
Silva et al. Page 5
Med Innov Bus. Author manuscript; available in PMC 2010 July 22.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
1. The document discusses the objectives, decision criteria, and performance of the University of Colorado's Proof of Concept programs, which provide funding to advance biomedical technologies.
2. It analyzes five models of early-stage technology development - university entrepreneurship, philanthropic funding, SBIR/STTR programs, business development organizations, and early-stage investment firms - to understand the context and criteria for POC funding decisions.
3. The primary
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This document outlines a preliminary framework for dimensions and indicators to monitor responsible research and innovation (RRI). It discusses six key RRI dimensions identified by the European Commission: public engagement, gender equality, science education, open access, ethics, and governance. The MoRRI consortium is developing indicators within each dimension to allow for assessment of RRI at the national and institutional level over time. Challenges include balancing RRI with research excellence, mainstreaming RRI practices, and determining how to best measure the benefits of RRI for science and society.
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This document discusses two important policy questions regarding science and innovation that require more data:
1) Tracking where highly trained students are placed after their education to better understand the role of education in fostering innovation and the transfer of knowledge to firms. Existing data focuses too much on placements in academia.
2) Collecting systematic cross-country data on the mobility of scientists and engineers to improve understanding of how mobility contributes to knowledge production and network formation, as well as the factors that influence mobility decisions. Existing mobility data is limited and misses information on those working in industry.
The document calls for international action to routinely collect standardized data on student placements by sector and mobility patterns of highly trained individuals and refugees to inform science
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This document presents a conceptual framework for understanding university spinoff activities. It discusses six streams of factors that influence spinoff behavior and performance: individual characteristics, organizational resources, institutional characteristics, environmental factors, performance and development phases, and economic impacts. These streams are explored through a review of relevant literature on academic entrepreneurship and commercialization. The framework aims to explain different aspects of spinoff activities in a coherent way and highlight their importance for economic development.
This document provides an overview of assessing the impacts of knowledge transfer from public research. It describes the different channels through which knowledge is transferred from public research institutions to industry, including collaborative research, contract research, labor mobility, and publishing research results. It also outlines some of the challenges in assessing these impacts, such as data quality issues, comparability of results, and identifying causal relationships. The document concludes that a combination of quantitative and qualitative data sources and methods is needed to fully understand and evaluate the overall impacts of public research.
by David H. Guston
Professor of Political Science
Director, Center for Nanotechnology in Society at ASU Co-Director, Consortium for Science, Policy & Outcomes.
Slides for meeting in Fondazione Bassetti
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Framework for understanding quantum computing use cases from a multidisciplin...Anastasija Nikiforova
This presentation is a supplementary material for the article "Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions" (Ukpabi, D.C., Karjaluoto, H., Botticher, A., Nikiforova, A., Petrescu, D.I., Schindler, P., Valtenbergs, V., Lehmann, L., & Yakaryılmaz, A) available at https://arxiv.org/ftp/arxiv/papers/2212/2212.13909.pdf. THe presentation, however, was delivered for QWorld Quantum Science Days 2023 | May 29-31.
The document summarizes two projects and three presentations related to scenarios, foresight, and knowledge platforms. It discusses:
1) A genomics workshop commissioned by a research council to inform funding decisions, including key drivers and themes identified. Scenarios developed explored potential impacts.
2) Lessons learned from the genomics exercise, including around timing, scope, and involvement of stakeholders. The value of technological aids and need to develop social science analysis methods was also noted.
3) A nanotechnology scenario workshop to develop visions of UK success in 2006 across six application areas. The workshop aimed to identify drivers of change and actions needed.
Facing the future: Sense-making in Horizon ScanningTotti Könnölä
The document summarizes a conference on horizon scanning and sense-making. It discusses how horizon scanning involves collecting observations of potential future developments and deriving policy implications. Sense-making is inseparable from scanning and involves perceiving, interpreting and constructing meaning from emerging trends. The case study described a horizon scanning exercise where 381 issues were identified, assessed, and synthesized into cross-cutting challenges to inform EU policymaking recommendations on sustainability, social changes, and governance.
An overview of how fundamental and use-inspired research and innovation are related. A presentation I made at the American Control Conference workshop on this topic.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Scope & Topics
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
Science, Technology and Innovation Outlook 2016 - EC/OECD Launch eventinnovationoecd
The document summarizes key points from the OECD Science, Technology and Innovation Outlook 2016. It discusses 8 megatrends that will impact science and innovation like aging societies and resource constraints. It also profiles 10 emerging technologies like artificial intelligence, biotechnologies, and the internet of things that will be important. The outlook notes challenges for governments in funding research due to competing priorities and calls for building international cooperation and more responsible innovation policies.
e-infrastructures supporting open knowledge circulation - OpenAIRE FranceJean-François Lutz
This document discusses e-infrastructures that support open access to scientific knowledge and data. It notes that science is becoming more collaborative globally and data-driven. E-infrastructures provide crucial enabling technologies for open data sharing, scientific workflows, and virtual collaborations. Future steps include further promoting open access policies and ensuring the long-term preservation and reuse of publicly-funded research outputs and data.
Big data impact on society: a research roadmap for Europe (BYTE project resea...Anna Fensel
With its rapid growth and increasing adoption, big data is producing a growing impact in society. Its usage is opening both opportunities such as new business models and economic gains and risks such as privacy violations and discrimination. Europe is in need of a comprehensive strategy to optimise the use of data for a societal benefit and increase the innovation and competitiveness of its productive activities. In this paper, we contribute to the definition of this strategy with a research roadmap that considers the positive and negative externalities associated with big data, maps research and innovation topics in the areas of data management, processing, analytics, protection, visualisation, as well as non-technical topics, to the externalities they can tackle, and provides a time frame to address these topics.
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1. Evaluating the effects of research and innovation investments on a national level is challenging due to the complexity of social systems and interaction of many factors.
2. Traditional linear models of innovation are limited and a systemic perspective is needed to understand how research contributes to economic and social outcomes through competence flows and learning.
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Presentation during the 14th Association of African Universities (AAU) Conference and African Open Science Platform (AOSP)/Research Data Alliance (RDA) Workshop in Accra, Ghana, 7-8 June 2017.
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• Invited speaker, Manchester Institute of Innovation Research, Manchester Business School, The University of Manchester, Manchester, United Kingdom, 17 February, 2009
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The document discusses the historical evolution of priority setting in science, technology, and innovation (STI) policy. It describes four major paradigms: 1) a mission-oriented approach from the postwar period focused on large-scale technologies; 2) an industrial policy approach emphasizing strategic technologies; 3) a systemic approach focused on the innovation system; and 4) a new mission-oriented approach engaging stakeholders on societal challenges. It analyzes trends toward more actors, decentralization, explicit strategies, and focus on broad missions and the innovation system. Challenges include establishing sound rationales, avoiding lock-in, ensuring coherence, and using strategic policy tools like foresight.
Rafols - Towards more inclusive STI indicatorsinnovationoecd
This document discusses the need for more inclusive science, technology, and innovation (STI) indicators that better capture diverse types of research and innovation.
Current STI indicators are biased towards certain types of mainstream science and may suppress or exclude valuable creative research in other fields like agriculture. This can threaten diversity in research. Indicators are also needed that make other contributions visible, like action research or co-creation.
While STI indicators can help with decisions, they do not necessarily lead to the "right" decisions if they do not reflect the full range of social and economic functions of science. Expanding indicator data and developing new indicator types may help broaden coverage of societal problems and peripheral areas of research.
Silva et al. Page 5
Med Innov Bus. Author manuscript; available in PMC 2010 July 22.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
1. The document discusses the objectives, decision criteria, and performance of the University of Colorado's Proof of Concept programs, which provide funding to advance biomedical technologies.
2. It analyzes five models of early-stage technology development - university entrepreneurship, philanthropic funding, SBIR/STTR programs, business development organizations, and early-stage investment firms - to understand the context and criteria for POC funding decisions.
3. The primary
Meijer - Monitoring the evolution and benefits of responsible research and in...innovationoecd
This document outlines a preliminary framework for dimensions and indicators to monitor responsible research and innovation (RRI). It discusses six key RRI dimensions identified by the European Commission: public engagement, gender equality, science education, open access, ethics, and governance. The MoRRI consortium is developing indicators within each dimension to allow for assessment of RRI at the national and institutional level over time. Challenges include balancing RRI with research excellence, mainstreaming RRI practices, and determining how to best measure the benefits of RRI for science and society.
Stephan - Science and Innovation Policy-making today: Big questions begging f...innovationoecd
This document discusses two important policy questions regarding science and innovation that require more data:
1) Tracking where highly trained students are placed after their education to better understand the role of education in fostering innovation and the transfer of knowledge to firms. Existing data focuses too much on placements in academia.
2) Collecting systematic cross-country data on the mobility of scientists and engineers to improve understanding of how mobility contributes to knowledge production and network formation, as well as the factors that influence mobility decisions. Existing mobility data is limited and misses information on those working in industry.
The document calls for international action to routinely collect standardized data on student placements by sector and mobility patterns of highly trained individuals and refugees to inform science
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This document presents a conceptual framework for understanding university spinoff activities. It discusses six streams of factors that influence spinoff behavior and performance: individual characteristics, organizational resources, institutional characteristics, environmental factors, performance and development phases, and economic impacts. These streams are explored through a review of relevant literature on academic entrepreneurship and commercialization. The framework aims to explain different aspects of spinoff activities in a coherent way and highlight their importance for economic development.
This document provides an overview of assessing the impacts of knowledge transfer from public research. It describes the different channels through which knowledge is transferred from public research institutions to industry, including collaborative research, contract research, labor mobility, and publishing research results. It also outlines some of the challenges in assessing these impacts, such as data quality issues, comparability of results, and identifying causal relationships. The document concludes that a combination of quantitative and qualitative data sources and methods is needed to fully understand and evaluate the overall impacts of public research.
by David H. Guston
Professor of Political Science
Director, Center for Nanotechnology in Society at ASU Co-Director, Consortium for Science, Policy & Outcomes.
Slides for meeting in Fondazione Bassetti
Dr. Ozcan Saritas presented on how foresight can help achieve the United Nations Sustainable Development Goals (SDGs). The SDGs present an ambitious vision for development by 2030 but implementing them will be complex due to their interconnections and unknown future challenges. Foresight uses systematic, participatory processes to gather future intelligence and build long-term visions that can inform present-day decisions and mobilize joint actions. It can help assess country contexts, set national and regional SDG targets and strategies, and monitor and evaluate progress over time by comparing foresight outcomes. Foresight outputs like visions, intelligence, and evidence-based guidelines can benefit stakeholders across government, business, science, education and society.
Framework for understanding quantum computing use cases from a multidisciplin...Anastasija Nikiforova
This presentation is a supplementary material for the article "Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions" (Ukpabi, D.C., Karjaluoto, H., Botticher, A., Nikiforova, A., Petrescu, D.I., Schindler, P., Valtenbergs, V., Lehmann, L., & Yakaryılmaz, A) available at https://arxiv.org/ftp/arxiv/papers/2212/2212.13909.pdf. THe presentation, however, was delivered for QWorld Quantum Science Days 2023 | May 29-31.
The document summarizes two projects and three presentations related to scenarios, foresight, and knowledge platforms. It discusses:
1) A genomics workshop commissioned by a research council to inform funding decisions, including key drivers and themes identified. Scenarios developed explored potential impacts.
2) Lessons learned from the genomics exercise, including around timing, scope, and involvement of stakeholders. The value of technological aids and need to develop social science analysis methods was also noted.
3) A nanotechnology scenario workshop to develop visions of UK success in 2006 across six application areas. The workshop aimed to identify drivers of change and actions needed.
Facing the future: Sense-making in Horizon ScanningTotti Könnölä
The document summarizes a conference on horizon scanning and sense-making. It discusses how horizon scanning involves collecting observations of potential future developments and deriving policy implications. Sense-making is inseparable from scanning and involves perceiving, interpreting and constructing meaning from emerging trends. The case study described a horizon scanning exercise where 381 issues were identified, assessed, and synthesized into cross-cutting challenges to inform EU policymaking recommendations on sustainability, social changes, and governance.
An overview of how fundamental and use-inspired research and innovation are related. A presentation I made at the American Control Conference workshop on this topic.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Scope & Topics
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
Science, Technology and Innovation Outlook 2016 - EC/OECD Launch eventinnovationoecd
The document summarizes key points from the OECD Science, Technology and Innovation Outlook 2016. It discusses 8 megatrends that will impact science and innovation like aging societies and resource constraints. It also profiles 10 emerging technologies like artificial intelligence, biotechnologies, and the internet of things that will be important. The outlook notes challenges for governments in funding research due to competing priorities and calls for building international cooperation and more responsible innovation policies.
e-infrastructures supporting open knowledge circulation - OpenAIRE FranceJean-François Lutz
This document discusses e-infrastructures that support open access to scientific knowledge and data. It notes that science is becoming more collaborative globally and data-driven. E-infrastructures provide crucial enabling technologies for open data sharing, scientific workflows, and virtual collaborations. Future steps include further promoting open access policies and ensuring the long-term preservation and reuse of publicly-funded research outputs and data.
Big data impact on society: a research roadmap for Europe (BYTE project resea...Anna Fensel
With its rapid growth and increasing adoption, big data is producing a growing impact in society. Its usage is opening both opportunities such as new business models and economic gains and risks such as privacy violations and discrimination. Europe is in need of a comprehensive strategy to optimise the use of data for a societal benefit and increase the innovation and competitiveness of its productive activities. In this paper, we contribute to the definition of this strategy with a research roadmap that considers the positive and negative externalities associated with big data, maps research and innovation topics in the areas of data management, processing, analytics, protection, visualisation, as well as non-technical topics, to the externalities they can tackle, and provides a time frame to address these topics.
Koch taftie-measuring the effects of researchPer Koch
1. Evaluating the effects of research and innovation investments on a national level is challenging due to the complexity of social systems and interaction of many factors.
2. Traditional linear models of innovation are limited and a systemic perspective is needed to understand how research contributes to economic and social outcomes through competence flows and learning.
3. Both quantitative and qualitative methods must be combined to measure direct and indirect outcomes of policies and understand the context in which innovation occurs.
Presentation during the 14th Association of African Universities (AAU) Conference and African Open Science Platform (AOSP)/Research Data Alliance (RDA) Workshop in Accra, Ghana, 7-8 June 2017.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
This document summarizes a journal that provides a forum for researchers working on extracting useful information from growing volumes of digital data using new computational theories and tools. The journal accepts articles on advances in topics like data mining algorithms, text and multimedia mining, knowledge representation, and statistical techniques. Authors are invited to submit original papers by May 16, 2020 following the journal's submission guidelines.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
The document discusses a journal that provides a forum for researchers working on extracting useful information from growing volumes of digital data using new computational theories and tools for data mining and knowledge discovery. It solicits article submissions on topics related to data mining algorithms, techniques for text, multimedia and web data, databases, visualization, and more. Authors are invited to submit original papers by certain deadlines for peer-reviewed publication in the open access journal.
OSi Geographic Information Research & Development Initiatives Launch
Ordnance Survey Ireland GI R&D Initiatives
Tuesday, 22 March 2016, 13:00 to 20:30 (GMT) , Maynooth University
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
Supervised Multi Attribute Gene Manipulation For Cancerpaperpublications3
Abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems.
They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery.
This document discusses the promise and challenges of data analytics in healthcare and biomedical research. It notes that we are at a point of deception, where digitization is disrupting traditional models through increased data volume, velocity and variety. The document outlines NIH's Big Data to Knowledge initiative to accelerate biomedical discovery through open data sharing and improved analytics. Precision medicine is highlighted as one area that could see major breakthroughs through these approaches. Challenges around data standards, privacy, workforce needs and demonstrating value are also discussed.
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IFI Seminar - Foresight for Science, Technology & Innovation
1. Foresight
for Science, Technology & Innovation - ForSTI
Dr. Ozcan Saritas
osaritas@hse.ru
National Research University Higher School of Economics
2. 2
Why are we concerned about Foresight?
Setting priorities
Optimizing resource use
Building visions
Thinking out of the box
Detecting Weak Signals of emerging trends
Giving lead time for innovators
Enabling innovation
Preparing for Wild Cards
Exploring alternative futures
Formulating policies & strategies
Networking stakeholders
Mutual learning
Collecting intelligence
Making evidence-based decisions
Simply because it is too costly not to do so!!!
3. 3
What is Foresight?
“the application of
‘systematic’,
‘participatory’,
‘future-intelligence-gathering
and medium-to-long-term
vision building process’ to
‘informing present-day
decisions and mobilising joint
actions’”
4. 4
Forecasting vs. ‘Foresight’
Futures thinking: Single vs. Multiple
Nature of situations: Simple, Complicated vs. Complex
Nature of systems: Technological and Economic only vs. STEEPV
Nature of problems & approaches: Positivist vs. Constructivist &
Critical
Level of participation: Expert driven vs. participative & inclusive
Level of uncertainty & Time horizons: Low uncertainty, short term
vs. High uncertainty, long term
Taking into account of Wild Cards / Surprises / Shocks: No
contingency vs. High contingency
Use of techniques: Quantitative vs. Quan. & Qual. and their
combinations
5. 5
Evolution of Foresight practice
Early
Foresight
1950s 1960s 1970s 1980s
1990s 2000s 2010s 2020s Future
Beginning of Civilization Post World War Recurrent oil
shocks
Emergence of
Innovation as an
economic driver
Economic crises &
climate change,
energy, and security
issues
S&T as a strategic
instrument with
economic & social
benefits
Increasing frequency
of disasters, shocks,
surprises
Rapid technological
advancements
6. 6
Foresight from the past
Jean-Marc Côté's Visions of the Year 2000 (1899)
http://www.paleofuture.com/blog/category/1890s
6
7. 7
Trends in the use of ForSTI methods
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1991 1992 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
technology foresight Scenario analysis Delphi method
Perfect foresight Technology forecasting Strategic foresight
Decision making Technology road map Mental time travel
System Dynamics Models hindsight bias game theory
multi-criteria decision analysis policy analysis risk assessment
science and technology policy Simulation bibliometric analysis
Adaptive foresight Emerging technology Expert panel
Weak signal Text mining Case study
cluster analysis Network analysis nonlinear systems
patent analysis Stochastic model trend research
Autobiographical memory Early warning Horizon scanning
Megatrends path dependence priority setting
Survey agent-based modeling Analytic hierarchy process
cross-impact analysis Dynamic programming Future-oriented technology analysis
Metacognition monitoring Neuroimaging
Portfolio management Strategic decision making workshop
Alternative futures Bayesian estimation Benchmarking
benefit-cost analysis content analysis Evidence-based policy
Saritas, O. and Burmaoglu, S. (2015). The evolution of the use of Foresight methods: a scientometric analysis of global FTA research output, Scientometrics, 105, 1, 497-508.
8. 8Saritas, O. and Burmaoglu, S. (2015). The evolution of the use of Foresight methods: a scientometric analysis of global FTA research output, Scientometrics, 105, 1, 497-508.
1991-2000
2001-2010 2010-present
Integration of ForSTI methods
12. 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Pre
Foresight Technology
Foresight –
Foresight First
Cycle Foresight
Second
Cycle
Foresight Third Cycle ????
Office of S&T (1992-2006) of Science & Innovation (2006-7)
Located: till 1995 Cabinet Office
Department of Trade and Industry till 2006
Department of Innovation, Universities and Skills till 2007..
Office of
Chief
Scientist
D.E.S.
Science
Office
Govt. Office for Science.
Located: Department of
Business, Innovation & Skills
Party in Power:
Coalition:
Conservative (Major) Labour (Blair to 2007, then Brown) Con-Dem
Three cycles of Foresight in the UK
12
15. S&T Foresight
System
•Evidence -based analysis
•Integration of quantitative
and qualitative methods
•Prioritizing
•Communication and
networking
•Stakeholder engagement
•Integration to policy
Global Challenges
Grand Responses
Demand for new
skills
Policy mix
New economic
paradigm
New
instruments
for STI policy
Changing
society
Multidisciplinary
and multicultural
research
Changing National
Innovation Systems
Enhancing Infrastructure,
networks
Changing global value
chains and traditional
leaders
Globalization vs
localization
Increasing
influence of
technologies
Russian Foresight system
15
16. Evolution of Foresight activities in Russia – with a
boost
2008 2009 2010 2011 2012 2013 20142006 2007
Critical
technologies
(national)
S&T Foresight 2025 S&T Foresight 2030
STI priorities for natural resources
Foresight for nanoindustry markets
Foresight for ICT
and mass-media
2030
STI priorities for
Bashkortostan
STI priorities for
Moscow
STI priorities for
Samara
Tomsk
innovation
infrastructure
STI priorities
for Tula
Regional clusters
Roadmap for power
engineering equipment
Sectoral roadmaps for new materials
(space, aircraft, nuclear energy)
Roadmap for medicine &
pharmaceutical industry
Roadmap for water purification
Roadmap for composite materials
Roadmaps for energy efficiency
Roadmaps for oil & gas sector: upstream & downstream
Roadmaps for technology platforms
Programmes of innovative development of
state-owned companies: priorities,
roadmaps, technology audit, et al.
Foresight for
shipbuilding
Critical technologies (national)
Software – Interaction with expert network
Online database on global technology trends
Software – Interactive
technology roadmaps
Demand for future skills
Foresight for civil society
Federal level
Regional level
Sectoral and
corporate level
Foresight
infrastructure
1996 – 1997: Initiation of
Foresight projects in Russia
(HSE team)
Roadmap
for space
navigation
S&T Foresight for
aircraft sector
Concept of a
roadmap for
automotive
industry: FCVRoadmap for LED manufacturing
Regional
Foresight
(education)
2015-2018
National technology
Foresight system
Critical technologies
(national)
S&T Foresight
2040
Roadmaps for aviation
Sectoral Foresight
systems
Critical technologies
(sectoral)
Network of
sectoral S&T
Foresight centres
Foresight for
National
universities
(5/100)
Evolution of Foresight activities in Russia
16
17. Russian S&T Foresight system
Russian S&T Foresight
Forecast of socioeconomic
development
Budget forecast
Socioeconomic
development strategy
for Russia
Strategic goals and priorities of
socioeconomic and S&T
development (President’s
addresses)
Strategies for macroregions
State S&T development programme
Strategic Foresight
Priority S&T areas and critical technologies for Russia
Spatial development strategies
Programmes for
regional clusters
Regionallevel
Sectoral strategies
Sectoral S&T priorities
and critical technologies
Sectoral S&T Foresight studies
Sectorallevel
State regional programmes
Companies’
programmes
and target
documents
Strategic R&D
programmes for
technology
platforms
Technology
roadmaps Sectoral state
programmes
Nationallevel
Russian S&T Foresight system
17
18. More than 150 global
trends in the
economy, science,
politics and society
Assessments of
effects and
periods of
maximal
manifestation of
challenges and
windows of
opportunities
More than 1000
specific priority
R&D tasks
Characteristi
cs of more
than 80
prospective
markets and
250 product
groups
Priority S&T areas Key sectors of the economy Publications
Russian Foresight outputs
18
19. 19
Global Trends
Growing up: global demand for products and
services
Getting scarce: water, food, energy, natural
resources
Gaining value: scarce resources become more
expensive as they are finite and shared
Giving rise to: sustainability and security
concerns problems with potential social,
economic and political conflicts
Going technological: Advancements in STI
and increasing public and private R&D are
providing opportunities for addressing global
challenges
24. 24
ForSTI: a systemic process
Miles, I., Saritas, O. and Sokolov, A. (2016). Foresight for Science, Technology and Innovation, Springer Verlag, Berlin.
Initiation: establishing the purpose of the activity, its scope and
intended uses and users, and the resources that are available
Intelligence: Scanning the focal object and its context,
establishing basic knowledge about trends, about the results of
other studies and the views of major stakeholders, etc.
Imagination: Involving efforts to grasp the underlying dynamics of
the focal object, to map and model it for the future
Integration: Delineating and appraising possible futures that can
arise from the dynamics considered
Interpretation: Examining the implications of the analysis, and
suggesting relevant strategies and priorities for achieving the
major objectives of the sponsor and other stakeholders
Intervention: Communicating these interpretations and steps that
follow to key actors
Impact: Evaluating the extent to which the ForSTI activity has
achieved its objectives and been of use, and examining follow-up
and the scope for embedding such activity in the organisations
concerned
Interaction: an activity that early on particularly involves
recruitment of stakeholders, and later engages them in
participation in successive phases of the process
25. 25
A Systemic Foresight process
Initiation
Intelligence
Imagination
Integration
Intervention
Impact
Interaction
Horizon Scanning
Literature review
Big Data & STI mining
Social Network Analysis
Systems Mapping
Scenario Planning
ModellingGaming
Delphi
Multi-criteria analysis
Success scenarios
SWOT analysis
Visioning
Roadmapping
Backcasting
Strategic planning
Critical/key technologies
Forecasting
Scoping
Priority-setting
Policy assessment
Survey
Interviews
OR methods
Stakeholder mapping
Expert panels
Workshops
Action planning
Environmental Scanning
Voting
Polling
Indicators
Brainstorming
Interpretation
FORSTAR – Foresight process & methods
Miles, I., Saritas, O. and Sokolov, A. (2016). Foresight for Science, Technology and Innovation, Springer Verlag, Berlin.
28. 28
Using Big Data for Foresight
Data is our major asset!!!
Analysis of scientific
literature & media
Patent analysis
Big data intelligent analysis system –
intelligent FOResight Analytics (iFORA)
Scientific
publications
> 2 mln
Scientific articles
> 10,000
Research fronts
Experts
> 10,000
Patents
> 500,000
Analytical
reports &
Forecasts
> 50,000
News feeds
> 1000
International
conferences
> 150
Combination of
quantitative &
qualitative methods
Analysis of networks &
clusters
Focus groups and in-
depth interviews
Biblio-/Sciento-metric
analysis
Semantics &
Text mining
Expert consultations
STEEPV analysis
Funding
NSF, DARPA,
EC, ESRC…
Stock Exchange
DJ, FTSE,
NIKKEI…
Mergers &
Acquisitions
Thomson
Reuters
29. 29
How the Victorians invented the future…
http://aeon.co/magazine/society/how-the-victorians-imagined-and-invented-the-future/
30. 30
Second mission of Foresight:
Analyzing wider impacts of change
Revolutions in machinery,
manufacturing &
transportation Slum
days, many suffered ill
health due to starvation in
Victorian Britain, such as
these London children in
1860…
Enormous expansion of rail
and telegraph lines,
unprecedented movement of
people and ideas, a new
wave of globalization and
destruction…
Increased access to goods
and services stress on
natural resources,
demographic change &
the emergence of
megacities…
1st Industrial revolution 2nd 3rd