In this presentation we show the results of a case study in which we tried to analyze the extent to which business funds received by universities correlate to the university-industry co-publications. The case study focuses on the technical university of Valencia.
A discussion 'think piece' presented by Professor Lynn Martin at an innovation workshop hosted by the West Midlands Regional Observatory in Birmingham on 19 March 2009.
Exploring the relationship between r&d, education and entrepreneurshipXiayu (Carol) Zeng
The study investigates the relationship between R&D, education and entrepreneurship at a global level from 2005 to 2012, by performing three types of analysis: descriptive analysis, correlation analysis and predictive analysis.
Tool/Software: IBM Cognos
Area of Work: Innovation
Title: Key Factors influencing Countries Innovation
Objectives: Evaluating the Components that contribute to a county’s innovation Performance.
For efficient use of resources in those areas, will help developing countries to emerge on a fast track.
Methodology: Using Regression model (panel data analysis)
A discussion 'think piece' presented by Professor Lynn Martin at an innovation workshop hosted by the West Midlands Regional Observatory in Birmingham on 19 March 2009.
Exploring the relationship between r&d, education and entrepreneurshipXiayu (Carol) Zeng
The study investigates the relationship between R&D, education and entrepreneurship at a global level from 2005 to 2012, by performing three types of analysis: descriptive analysis, correlation analysis and predictive analysis.
Tool/Software: IBM Cognos
Area of Work: Innovation
Title: Key Factors influencing Countries Innovation
Objectives: Evaluating the Components that contribute to a county’s innovation Performance.
For efficient use of resources in those areas, will help developing countries to emerge on a fast track.
Methodology: Using Regression model (panel data analysis)
Report of perspectives from 102 industry leaders on how they approach and value university relationships for innovative collaborations. Report from 18 high-tech sectors and businesses of all sizes
EngD research impacts - summary of key findingsAEngD
EngD research impacts - summary of key findings: presentation delivered by Dr Fumi Kitagawa (Manchester Business School) at the AEngD conference 2013, held at Building Centre, London on 26 September 2013
Design of Success Criteria Based Evaluation Model for Assessing the Research ...Waqas Tariq
Innovations and inventions are not outcomes of single activity of any organization. This is a result of collaboration of different partners. Collaborated research of university and industry can enhance the ability of scientist to make significant advances in their fields. The evaluation of collaborated research between university and industry has created the greatest interest among the collaborational researchers because it can determine the feasibility and value of the collaboration. This paper intends to illustrate the evaluation metrics and success criteria- based evaluation model in between university-industry in their collaborated research. For bridging the model, the success criteria have been identified based on key evaluation metrics. A successful Collaboration of university and industry is not dependent on any single metric but instead on the confluence of multiple metrics from the growth of basic research to commercialization. This study is intended to provide different evaluating metrics to impound the research collaboration constraints between university and industry, and design success criteria to upsurge the successful linkage. For this purpose we have developed constraints and success criteria based evaluation metrics (CASEM) model. The proposed model is appropriate for almost all types of collaborations specially research collaborations between university and industry. By adopting this model, any university or industry can easily cross the threshold in the grown-up research collaborational community.
Building Higher Education curricula fit for the future: how HE institutions are responding to the Industrial Strategy
Presented at the Anglia Learning & Teaching Annual Conference, Engage, on 25 June 2019 by Dr Simon Pratt-Adams (Director of CIHE) and Dr Emma Coonan (Research Fellow, CIHE)
Research collaboration fostering mutual value of universities and sm es 2016 ...Kari Laine
Presentation held at University-Industry Interaction Conference,
The largest global event centered on university-industry interaction, entrepreneurial universities and collaborative innovation.
2012.06.13 Economic Growth and Academic Entrepreneurship: Lessons and Implica...NUI Galway
Professor Donald Siegel, University at Albany, State University of New York, presented the second keynote address "Economic Growth and Academic Entrepreneurship: Lessons and Implications for Industry, Academia and Policymakers" at the IntertradeIreland All-Island Innovation Programme annual conference 2012, Exploiting Industry and University Research, Development and Innovation: Why it Matters held at National University of Ireland, Galway, 12 - 13 June 2012. Part 1
2012.06.13 Economic Growth and Academic Entrepreneurship: Lessons and Implica...NUI Galway
Professor Donald Siegel, University at Albany, State University of New York, presented the second keynote address "Economic Growth and Academic Entrepreneurship: Lessons and Implications for Industry, Academia and Policymakers" at the IntertradeIreland All-Island Innovation Programme annual conference 2012, Exploiting Industry and University Research, Development and Innovation: Why it Matters held at National University of Ireland, Galway, 12 - 13 June 2012. Part 2
Report of perspectives from 102 industry leaders on how they approach and value university relationships for innovative collaborations. Report from 18 high-tech sectors and businesses of all sizes
EngD research impacts - summary of key findingsAEngD
EngD research impacts - summary of key findings: presentation delivered by Dr Fumi Kitagawa (Manchester Business School) at the AEngD conference 2013, held at Building Centre, London on 26 September 2013
Design of Success Criteria Based Evaluation Model for Assessing the Research ...Waqas Tariq
Innovations and inventions are not outcomes of single activity of any organization. This is a result of collaboration of different partners. Collaborated research of university and industry can enhance the ability of scientist to make significant advances in their fields. The evaluation of collaborated research between university and industry has created the greatest interest among the collaborational researchers because it can determine the feasibility and value of the collaboration. This paper intends to illustrate the evaluation metrics and success criteria- based evaluation model in between university-industry in their collaborated research. For bridging the model, the success criteria have been identified based on key evaluation metrics. A successful Collaboration of university and industry is not dependent on any single metric but instead on the confluence of multiple metrics from the growth of basic research to commercialization. This study is intended to provide different evaluating metrics to impound the research collaboration constraints between university and industry, and design success criteria to upsurge the successful linkage. For this purpose we have developed constraints and success criteria based evaluation metrics (CASEM) model. The proposed model is appropriate for almost all types of collaborations specially research collaborations between university and industry. By adopting this model, any university or industry can easily cross the threshold in the grown-up research collaborational community.
Building Higher Education curricula fit for the future: how HE institutions are responding to the Industrial Strategy
Presented at the Anglia Learning & Teaching Annual Conference, Engage, on 25 June 2019 by Dr Simon Pratt-Adams (Director of CIHE) and Dr Emma Coonan (Research Fellow, CIHE)
Research collaboration fostering mutual value of universities and sm es 2016 ...Kari Laine
Presentation held at University-Industry Interaction Conference,
The largest global event centered on university-industry interaction, entrepreneurial universities and collaborative innovation.
2012.06.13 Economic Growth and Academic Entrepreneurship: Lessons and Implica...NUI Galway
Professor Donald Siegel, University at Albany, State University of New York, presented the second keynote address "Economic Growth and Academic Entrepreneurship: Lessons and Implications for Industry, Academia and Policymakers" at the IntertradeIreland All-Island Innovation Programme annual conference 2012, Exploiting Industry and University Research, Development and Innovation: Why it Matters held at National University of Ireland, Galway, 12 - 13 June 2012. Part 1
2012.06.13 Economic Growth and Academic Entrepreneurship: Lessons and Implica...NUI Galway
Professor Donald Siegel, University at Albany, State University of New York, presented the second keynote address "Economic Growth and Academic Entrepreneurship: Lessons and Implications for Industry, Academia and Policymakers" at the IntertradeIreland All-Island Innovation Programme annual conference 2012, Exploiting Industry and University Research, Development and Innovation: Why it Matters held at National University of Ireland, Galway, 12 - 13 June 2012. Part 2
The paper presents an organizational framework and a methodology toolkit that tackles one of the major hurdles of economic development in South East Europe (SEE), the missing link of the innovation triple helix: the valorisation of research performed in the region’s universities and research centres.
Manufacturing is going through what many call the 4th industrial revolution with new technologies coming into place to disrupt the way we produce, distribute and sell goods in contemporary markets.
This paper presents an EU support action for Learning Analytics Community Exchange (LACE) which includes a specific work package dedicated to the study and promotion of innovative learning analytics approaches in the manufacturing work place. It presents an innovative training process based on maturity modelling called MAN.TR.A.(tm) which will be used in the LACE 30 months life cycle to detect, classify and analyze best practices in manufacturing training aimed at identifying process models and appraisal paths for those seeking excellence in the manufacturing training sector.
Kornelia Konrad-La empresa y las políticas de innovación transformadorasFundación Ramón Areces
El 25 de abril de 2017 organizamos en la Fundación Ramón Areces una mesa redonda sobre 'La empresa y las políticas de innovación transformadoras'. En este foro participaron, entre otros, Totti Konnola, CEO de Insight Foresight Institute; Luis Fernando Álvarez-Gascón Pérez, Director General GMV secure eSolutions; y Francisco Marín, Director General del CDTI. Esta actividad se celebró en colaboración con el Grupo de Investigación en Economía y Política de la Innovación de la Universidad Complutense de Madrid (GRINEI-UCM) y el Foro de Empresas Innovadoras (FEI).
Collaboration between universities and industry NZ and Australialorraine skelton
Knowledge nations are keeping knowledge to themselves.
A report by Deloitte 2018 shows that less than 5% of industry forms collaborations with universities in NZ and only 3% in Australia, which puts us a long way behind other developed countries.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
1. INSTITUTE OF INNOVATION AND KNOWLEDGE MANAGEMENT
Do university-industry co-publication volumes correspond with university funding from business firms?
Joaquín M. Azagra-Caro | Leiden, 4 September 2014
Co-authors: Alfredo Yegros-Yegros, MayteLópez-Ferrer, Robert J.W. Tijssen
2. Introduction
•University-industry interactions are a slightly controversial source of potential benefits, among other, to partially contribute to economic development
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•Different indicators to monitor and foster university- industry interactions (contract research, R&D projects, patent licenses, creation of start-up companies…)
Major drawback Not freely available
3. University-industry co-publications (UICs)
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•One of the very few sources for gathering aggregate- level proxy measures of university-industry interaction patterns and trends (Tijssen et al., 2009; Tijssen, 2011)
•Some studies rely entirely on UICs to capture university-industry interactions
–Analyses of UICs (Calvert and Patel, 2003; Ponds et al 2007)
–Effect of UICs on university commercialization technology (Wong and Singh, 2013)
4. Validity of UICs as proxy of university- industry interactions
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•UICs are a particular type of co-authorship, only partial indicators of research collaboration (Katz and Martin, 1997)
–One third of the companies providing funding to the university had not UICs with the university –only 16% of the companies publishing UICs also provided funding (Lundberg et al. 2006)
•Previous attempts of validation are scarce
–Collaborations might not produce UICs
–Some UICs might not necessarily entail collaboration
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5. Research question
•Our objective is to contribute to prove whether or not UICs are good proxies of university-industry interactions
5
DoUICvolumescorrespondwithuniversityfundingfrombusinessfirms?
•Researchquestion
Business funding is associated with some of the most frequently occurring university-industry interactions (e.g. contract research or joint research agreements) (Roessner, 1993)
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•Needofaconceptualframework
6. Four types of theoretical relationships between funding and UIC –an interactive model
6
University funding from business firmst-τ
UICt
University funding from business firmst+φ
University funding from business firmst
Industry financing
University signalling
Industry pull
Science push
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7. Data
•Polytechnic University of Valencia (UPV)
•247 UPV authors of UICs in 2008-2011 (Source: WoS)
•UPV researchers of projects with firms in three periods (Source: UPV technology transfer office)
–1,224 in 2000-2007
–1,004 in 2008-2011
–482 in 2012-2013
•Name matching of both databases
•Project data includes amount of funding
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8. Types of funding and UIC relationships at UPV
8
Industry financing
University signalling
Industry pull
Science push
94%
6%
UPV participants in projects with firms 2000-2007
Non-UICauthors
UIC authors
93%
7%
UPV participants in projects with firms 2008-2011
Non-UICauthors
UIC authors
83%
17%
UPV UIC authors
Non-participantsin projects withfirms 2012-2013
Participants inprojects withfirms 2012-2013
72%
28%
UPV UIC authors
Non-participantsin projects withfirms 2008-2011
Participants inprojects withfirms 2008-2011
University funding from business firms2000-2007
UIC2008-2011
University funding from business firms2012-2013
University funding from business firms2008-2011
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9. Pairwise correlation coefficients (R)
9
•Non-significant statistical relationships
Mean business project funding
(2000-2007)
Mean business project funding
(2008-2011)
# UICs 2008-2011
R = 0.01
R = -0.01
# UICs 2008-2011
(funding 2008-2011)
# UICs 2008-2011
(funding 2012-2013)
Mean business project funding
R = -0.01
0.15
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11. Let’s start with the left half of the picture
11
Industry financing
Industry pull
University funding from business firms2000-2007
UIC2008-2011
University funding from business firms2008-2011
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12. Econometric models of financing and industry pull
•UIC = f(university funding from business firms)
•But UIC=0 may mean two things:
–Authors wanted to produce UIC and could not, e.g. for confidentiality or lack of scientific novelty
–Authors did not want to produce UICs, e.g. they used funding for other purposes or lack of environmental culture
•Correct verification of impact of business funding in two steps:
–Step 1 UIC(yes/no) = f (university funding from business)
–Step 2 For UIC>0, #UIC = f (university funding from business)
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13. Heckman selection models of financing and industry pull at the UPV
13
•No sample selection bias
•No sign of financing or industry pull effects at the UPV
Step
Dependent variable
Coefficient of mean business project funding
(2000-2007)
Coefficient of mean business project funding
(2008-2011)
1
UIC (yes/no) 2008-2011
-0.94
-0.18
(1.58)
(0.48)
2
# UIC 2008-2011
2.14
-2.01
(4.24)
(1.25)
# observations
1,224
1,004
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14. Let’s move to the right half of the picture
14
University signalling
Science push
UIC2008-2011
University funding from business firms2012-2013
University funding from business firms2008-2011
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15. Econometric models of signalling and science push
•University funding from business firms = f(UIC)
•But business funding=0 may mean two things:
–Researchers wanted business funding but did not get it, e.g. need of minimum scientific visibility
–Researchers did not want business funding, e.g. to preserve academic freedom
•Correct verification of impact of UICs on business funding in two steps:
–Step 1 Business funding(yes/no)=f(UIC)
–Step 2 For funding>0, amount of university funding from business firms=f(UIC)
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16. Heckman selection models of signalling and science push at the UPV
•Sample selection bias in science push
•Positive association between UIC and funding: high (science push) or borderline (signalling) –both in Step 2
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Step
Dependent variable
Coefficient of # UICs 2008-2011
(funding 2008-2011)
Coefficient of # UICs 2008-2011
(funding 2012-2013)
1
Business funding (yes/no)
-0.02
-0.05
(0.09)
(0.11)
2
Mean business funding
0.01***
0.12*
(0.00)
(0.01)
# observations
247
247
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17. Heckman selection models of signalling and science push at the UPV
•Sample selection bias in science push
•Positive association between UIC and funding: high (science push) or borderline (signalling) –both in Step 2
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Step
Dependent variable
Coefficient of # UICs 2008-2011
(funding 2008-2011)
Coefficient of # UICs 2008-2011
(funding 2012-2013)
1
Business funding (yes/no)
-0.02
-0.05
(0.09)
(0.11)
2
Mean business funding
0.01***
0.12*
(0.00)
(0.01)
# observations
247
247
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18. Do UIC output volumes correspond with university funding from business firms?
•In general, UICs can occur without business funding, and business funding without UICs – Answer: ‘no’
•For a minority of authors (those who participate in business funded projects), there is a positive association of current UICs and business funding –Answer: ‘yes’ (partial evidence of a science push)
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19. Conclusions
•Convenience of an interactive model to capture the complexity in the relationship between university funding from business firms and UICs
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•UICs as predicting factor: Wong and Singh (2013) also found a positive effect of UICs on university technology commercialization
•Scarce overlap between researchers participating in projects and publication of UICs (consistent with Lundberg et al, 2006)
•Studies based exclusively on UICs to analyse university-industry interactions do not fully capture business funded research –more evidence is needed
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20. Future research
•Analysis of the relationship of several project-related features and the generation of UICs (e.g. type of agreement, duration, gender…)
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•Broader approach, including any type of R&D activity and not only when the source of funding are business companies
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