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1 1. 2. Computer sciences, the Internet and mass media...7 1. 2. 1. Example of applying environmental principles...
The invention of the computer and the quick development of computer science has had a very significant impact on our lives. 1. 2. Computer science, the Internet and mass media Computers
computer science, social science, physics, biology, engineering, design, architecture and philosophy. A strong multidisciplinary and collaborative approach is the key requirement for large-scale technology innovation and the development of effective applications8.
and requires collaboration between the future users, computer science and IT as well as from the social sciences. Ambient assisted living involves technologies such as sensors, specific equipment, robotics, user interaction (multimodal interfaces) and simulation platforms.
Most computer science training teaches how to think about data (classifying things), whereas artificial intelligence enables us to learn how to think about knowledge (problem-solving).
A multidisciplinary approach ranging from computer science to molecular biology, integrating knowledge from the East and the West, is applied in this research.
example of eco-design process, Federated Conference on Computer science and Information systems, Krakow, Poland, 2013. MER 13b MERCIER-LAURENT E.,Innovating corporate management:
, 181 business model, 107 innovation, 62,126, 155 intelligence, 64,72, 113 watch, 79 computer science, 7 11,21, 26,69, 132 computers, 7, 13,15, 16,52
Computer science-Research and development, 23 (2), 47 65. Weske, M. 2007. Concepts, languages, architectures (Vol. 14. Berlin:
Just like computer science emerged as a new discipline from mathematics when computers became abundantly available,
Formal methods for web services (Lecture Notes in Computer science, Vol. 5569, pp. 42 88. Berlin:
In Sixth International Conference on Extending Database Technology (Lecture Notes in Computer science, Vol. 1377, pp. 469 483.
Proceedings of the 10th International Conference on Fundamental Approaches to Software engineering (FASE 2007)( Lecture Notes in Computer science, Vol. 4422, pp. 245 259.
International Conference on Business Process Management (BPM 2007)( Lecture Notes in Computer science, Vol. 4714, pp. 375 383.
Business Process Management Workshops, Workshop on Business Process Intelligence (BPI 2006)( Lecture Notes in Computer science, Vol. 4103, pp. 81 92.
Business Process Management (BPM 2010)( Lecture Notes in Computer science, Vol. 6336, pp. 211 226. Berlin:
Applications and Theory of Petri Nets 2010 (Lecture Notes in Computer science, Vol. 6128, pp. 226 245.
M. Dumas(*)F. M. Maggi Institute of Computer science University of Tartu, J. Liivi 2, Tartu 50409, Estonia e-mail:
Acknowledgments This work is supported by ERDF via the Estonian Centre of Excellence in Computer science. References Birukou
rmfl@cin. ufpe. br H. A. Reijers Department of mathematics and Computer science, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The netherlands e-mail:
He has a background in business economics und computer science and has obtained his economic doctorate degree from the Martin-Luther-University Halle-Wittenberg in 2013.
Curricula Vitae 293 Ricardo Massa F. Lima Federal University of Pernambuco, Brazil Ricardo Massa F. Lima received the Ph d. degree in computer science from Federal University
He is the Vice-Coordinator of UFPE's computer science postgraduate program. His main research interests include compiler construction and optimization
He received a Ph d. in Computer science in 2010 from University of Bari. Curricula Vitae 295 Monika Malinova Vienna University of Economics and Business, Austria Monika Malinova is a teaching
of Pernambuco (Recife, Brazil) in 2008 and his Ph d. degree in computer science from the Center for Informatics, Federal University of Pernambuco (Recife, Brazil), in 2014.
He received a Ph d. in Computer science (2002), A m. Sc. in Computer science (1994), and A m. Sc. in Technology and Society (1994), all from TU/e. Hajo wrote his Ph d. thesis on the topic of BPM for the service industry
Walter studied computer science at the Brandenburg University of Technology (BTU. His research activities include business process management, software development and graph theory.
evaluating all research groups in all Netherlands universities at the same time (e g. computer science; chemistry). ) In other disciplines (e g. physics), several evaluation committees were established,
Coursera, currently the biggest MOOC platform, was launched as a for-profit company in April 2012 by the two Stanford computer science professors Andrew Ng and Daphne Koller.
She is a computer engineer and Phd candidate, with an MA in Computer science at UNICAMP, Brazil.
Previously, she was Senior Researcher at CPQD in Brazil coordinating R&d projects related to the digital divide. 6 Dr. Maurizio Teli has recently been appointed as Research Fellow at the Department of Information Engineering and Computer science of the University of Trento (Italy.
and higher level skills wherever they are available on a worldwide basis, the separate disciplines of design, engineering, computer science,
She is a computer engineer and Phd candidate, with an MA in Computer science at UNICAMP, Brazil.
Previously, she was Senior Researcher at CPQD in Brazil coordinating R&d projects related to the digital divide. 6 Dr. Maurizio Teli has recently been appointed as Research Fellow at the Department of Information Engineering and Computer science of the University of Trento (Italy.
Also, in the case of Digital Business Ecosystem, an isomorphic model between biological behaviour and the behaviour of the software, based on theoretical computer science implications and leading to an evolutionary, self-organising,
Computer science is concerned with the construction of new languages and algorithms in order to produce novel desired computer behaviours.
Through the project Everyaware intends to integrate theoretical and practical techniques from the disciplines of environmental sensing, computer science,
A study based on a series of in depth interviews with central and peripheral Github users (carried out by the School of Computer science and the Center for the Future of Work, Heinz College and Carnegie mellon University;
and skills levels of prospective undergraduates applying to study Computer science. Upton has hypothesised that this drop in skills
Through the project Everyaware intends to integrate theoretical and practical techniques from the disciplines of environmental sensing, computer science,
A study based on a series of in depth interviews with central and peripheral Github users (carried out by the School of Computer science and the Center for the Future of Work, Heinz College and Carnegie mellon University;
and skills levels of prospective undergraduates applying to study Computer science. Upton has hypothesised that this drop in skills
Nortel alone accounts for almost 20 percent of all industrial R&d expenditures in Canada and hires one third of all Masters and Ph d. graduates in electrical engineering and computer science from Canadian universities.
thus a key success factor Many KETS require very specific skills, particularly cross-disciplinary knowledge from disciplines such as chemistry, physics, biology, computer sciences, mechanical engineering and material sciences.
Ru 1995 2000 Computer science, Lomonosov Moscow State university (MSU) Lars Mathiesen Nykredit 2002 today CIO and Executive vice president, Nykredit 1997 2002 Executive vice president Retail Business
(ICAA) Bachelor of Commerce with majors in Accountancy and Computer science, Deakin University Herman de Prins UCB 2009 today CIO
In collaboration with Columbia University computer science Professor Steven Feiner the author developed in the late 1990s real-world AR enhancements described as a situated documentary (Höllerer, Feiner, & Pavlik, 1999.
new informational systems and human resources training increase together with the enterprises'size. 2009 International Association of Computer science and Information technology-Spring Conference 978-0-7695-3653-8
27 Of the top 50 departments in the world in different subject areas, the majority are found in the USA (39 of 50 in computer science, 33 in engineering, 37 in neuroscience)( Technopolis, 2011;
CA in 1998 by Larry page and Sergey Brin while they were Phd candidates in computer science at Stanford university.
(ex Netscape), David Cheriton (Stanford computer science Professor), and Jeff Bezos41 (Amazon). In June 1999, a $25 million round of funding was announced,
36 5. 1. 2 Academic ranking of a Computer science Faculty...38 5. 1. 3 Employer Ranking of a Computer science Faculty...
40 5. 1. 4 Citations Ranking of a Computer science Faculty...42 5. 1. 5 R&d Expenditures by ICT Firms...
44 5. 1. 6 ICT FP7 Funding to Private Organisations...46 5. 1. 7 ICT FP7 Participations...
56 5. 1. 12 Scientific Publications in Computer science...58 5. 1. 13 Outward ICT R&d Internationalisation...
32 Academic ranking of a Computer science faculty Agrd 2 10 Employer ranking of a Computer science faculty Agrd 3 11 Citations ranking of a Computer science faculty Agrd 4 29 R&d
of ICT R&d centres Agrd 11 7 Scientific publications in Computer science Agrd 12 23 Internationalisation Outward ICT R&d internationalisation Intrd 1 5
18 Academic ranking of a Computer science faculty Agrd 2 7 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 6 R&d
of ICT R&d centres Agrd 11 16 Scientific publications in Computer science Agrd 12 4 Internationalisation Outward ICT R&d internationalisation Intrd 1 16
of a Computer science faculty Agrd 2 8 Employer ranking of a Computer science faculty Agrd 3 8 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT
centres Agrd 11 4 Scientific publications in Computer science Agrd 12 13 Internationalisation Outward ICT R&d internationalisation Intrd 1 4 Inward ICT
the EIPE ID card Activity Characteristic Name of Indicator Indicator ID Nr R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 1 Academic ranking of a Computer science
faculty Agrd 2 2 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT firms
R&d centres Agrd 11 11 Scientific publications in Computer science Agrd 12 12 Internationalisation Outward ICT R&d internationalisation Intrd 1 13 Inward
Academic ranking of a Computer science Faculty Table 15: Top ranking regions according to the Academic Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKH12
Cambridgeshire CC 100 5 2 UKJ14 Oxfordshire 87 19 3 UKI22 Outer London-South 73 114 4 UKM25 Edinburgh, City
UKE21 York 27 63 29 ITD55 Bologna 27 76 Indicator description Indicator ID Agrd 2 Name of indicator Academic ranking of a Computer science faculty
Measures the performance of the Computer science faculty according to the academic ranking of QS Unit of measurement The highest rank of a Computer science faculty in the academic ranking Definition of ICT dimension Computer science faculty Unit of observation NUTS
Frequency of the Academic Computer science faculty QS Ranking indicator values 1244 1 5 5 12 11 8 9 2 2 1 1
1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Academic ranking of a Computer science faculty Table 16:
Descriptive statistics of the Academic Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 38 7. 25 52.59 40 5
. 1. 3 Employer Ranking of a Computer science Faculty Table 17: Top ranking regions according to the Employer Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKH12
Cambridgeshire CC 100 5 2 UKJ14 Oxfordshire 95 19 3 UKI12 Inner London-East 68 2 4 UKI22 Outer London
30 GR300 Attiki 28 49 Indicator description Indicator ID Agrd 3 Name of indicator Employer ranking of a Computer science faculty What does it measure?
Measures the performance of the Computer science faculty according to the employer ranking of QS Unit of measurement The highest rank of a Computer science faculty in the employer ranking Definition of ICT dimension Computer science faculty Unit
Frequency of the Employer ranking of a Computer science faculty indicator values 1244 1 3 3 12 12 11 6 6 1 1 1 2 0
500 1000 1500 Frequency 0 20 40 60 80 100 Employer ranking of a Computer science faculty Table 18:
Descriptive statistics of Employer Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 47 7. 63 58.27 42 5
. 1. 4 Citations Ranking of a Computer science Faculty Table 19: Top ranking regions according to the Citations Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKL12
Gwynedd 100 266 2 PL127 Miasto Warszawa 91 50 3 NL335 Groot-Rijnmond 77 72 4 FR101 Paris 75 3
Gent 37 94 Indicator description Indicator ID Agrd 4 Name of indicator Citations ranking of a Computer science faculty What does it measure?
Measures the performance of the Computer science faculty according to the citations ranking of QS Unit of measurement The highest rank of a Computer science faculty in the citations ranking Definition of ICT dimension Computer science faculty Unit
Frequency of the Citations Computer science faculty QS Ranking indicator values 1243 3 11 10 9 6 7 3 2 3 2 2
1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Citations ranking of a Computer science faculty Table 20:
Descriptive statistics of Citations Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 94 9. 57 91.58 44 5
. 12 Scientific Publications in Computer science Table 35: Top ranking regions according to scientific publications in Computer science indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 NL333 Delft en
Westland 100 17 2 DE138 Konstanz 93 53 3 DE711 Darmstadt, Kreisfreie Stadt 89 7 4 UKI12 Inner London-East
Port Talbot 32 272 Indicator description Indicator ID Agrd 12 Name of indicator Scientific publications in Computer science What does it measure?
in the Computer science area produced by organisations located in the observed region Unit of measurement Region's share in the total number of publications in Computer science to a region's share in the EU population Definition of ICT dimension Computer science as defined by Web
Frequency of the scientific publications in Computer science indicator values 1172 38 18 21 13 8 12 2 3 4 1 2 1 2
Descriptive statistics of scientific publications in Computer science indicator Number of observations Mean value Standard deviation Variance 1303 2. 32 9. 45 89.45 60 5. 1. 13
of indicator Universities ranked in the QS University ranking Academic ranking of a Computer science faculty Employer ranking of a Computer science faculty Citations ranking of a Computer science faculty R&d expenditures by ICT firms ICT FP7 funding
Measures the number of universities in QS university ranking Measures the performance of the Computer science faculty according to the academic ranking of QS Measures the performance of the Computer science faculty according to the employer ranking of QS Measures the performance
of the Computer science faculty according to the citations ranking of QS Measures the average annual amount spent on R&d in the ICT sector Measures the amount received for research in ICT R&d Unit of measurement Region's share in the total
number of EU ranked universities to a region's share in the EU population The highest rank of a Computer science faculty in the academic ranking The highest rank of a Computer science faculty in the employer ranking The highest rank of a Computer science
ICT firms in the EU to a region's share in the EU population Region's share in the total EU ICT FP7 funding to a region's share in the EU population Definition of ICT dimension None Computer science faculty Based on NACE
Scientific publications in Computer science What does it measure? It measures the total number of ICT R&d FP7 projects to which organisations,
in the Computer science area produced by organisations located in the observed region Unit of measurement Region's share in the total number of ICT FP7 participations to a region's share in the EU population Region's share in the total EU ICT
in Computer science to a region''s share in the EU population Definition of ICT dimension ICT areas of the FP7 programme Based on HIS isuppli classification of the major"semiconductors influencers"Computer science as defined by Web of Science classification of Research
Areas Unit of observation NUTS 3 Source ICT FP7 by EC DG CONNECT (see Section 8. 2) R&d Centre location by IHS isuppli (Section 8
Data Sources 8. 1 QS WORLD UNIVERSITY RANKINGS by QS The Computer science and Electronic Faculties rankings originate from the QS WORLD UNIVERSITY RANKINGS,
which there is the Computer science subject considered appropriate for the EIPE study. To construct measures of faculty performance,
For the purpose of the EIPE exercise, journals classified in the Computer science research area are considered. 8. 4 R&d Centre Location by IHS isuppli The data used for the purpose of identification of R&d centre locations
o Computer science and engineering with respect to university faculties, o Computer science with respect to scientific publications, o ICT hardware and software with respect to R&d activity performed in R&d centres,
the EIPE ID card Activity Characteristic Name of Indicator Indicator ID Nr R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 1 Academic ranking of a Computer science
faculty Agrd 2 2 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT firms
11 Scientific publications in Computer science Agrd 12 12 Internationalisation Outward ICT R&d internationalisation Intrd 1 13 Inward ICT R&d internationalisation Intrd
of indicator Universities ranked in the QS University ranking Academic ranking of a Computer science faculty Employer ranking of a Computer science faculty Citations ranking of a Computer science faculty R&d expenditures by ICT firms FP7 funding
Measures the number of universities in QS university ranking Measures the performance of the Computer science faculty according to the academic ranking of QS Measures the performance of the Computer science faculty according to the employer ranking of QS Measures the performance
of the Computer science faculty according to the citations ranking of QS Measures the average annual amount spent on R&d in the ICT sector Measures the amount received for research in ICT R&d Unit of measurement Region's share in the total
number of EU ranked universities to a region's share in the EU population The highest rank of a Computer science faculty in the academic ranking The highest rank of a Computer science faculty in the employer ranking The highest rank of a Computer science
ICT firms in the EU to a region's share in the EU population Region's share in the total EU FP7 funding to a region's share in the EU population Definition of ICT dimension None Computer science faculty Based on NACE Rev
of indicator FP7 participations FP7 funding to SMES FP7 participations by SMES Location of ICT R&d centres Ownership of ICT R&d centres Scientific publications in Computer science
in the Computer science area produced by organisations located in the observed region Unit of measurement Region's share in the total number of FP7 participations to a region's share in the EU population Region's share in the total EU FP7 funding
's share in the EU population Region's share in the total number of R&d centres owned by EU firms to a region's share in the EU population Region's share in the total number of publications in Computer science to a region's share in the
. 4) Computer science as defined by Web of Science classification of Research Areas Unit of observation NUTS 3 Source FP7 database by EC DG Connect (see Section 5. 2) ICT
The performance of universities and computer science faculties across the world, as reported by the QS University ranking.
measured in terms of the number of publications in the computer science research area, of the research institutions in Europe for the period 2000-2012 from the Web of Science by Thomson Reuters. For a detailed description of the data source, see Section 5. 3. 20 Company-level
and Computer science and Electronic Faculties originate from the QS WORLD UNIVERSITY RANKINGS. It was formed in 2008 to meet the increasing public interest in comparative data on universities and organisations,
of which is Computer science, additional faculty-level information is extracted for the purpose of the EIPE study.
including Computer science. This information allows us to observe the location of research and education in ICT activities at world-level.
For the purpose of the EIPE exercise, journals classified in the Computer science research area are considered.
which permits the inclusion of EIPE-relevant fields such as Computer science. This information allows us to observe the location of ICT R&d activity.
or GIGO for short, is a computer science concept that refers to the fact that the veracity of the output of any logical process depends on the veracity of the input data.
when it comes to working with large volumes of big data calling for computer science and decision-analysis skills that are emphasized not in traditional statistical courses (Mcafee and Brynjolfsson,
Based on the analysis of the curriculum vitae of the top 1, 000 scientists in computer science, it shows that these conditions were met only in the US academic system.
particularly computer science, and industrial competitiveness. We will use original evidence, admittedly of preliminary type, to support this proposition.
In the fourth section we review descriptive evidence drawn from a large sample of CVS of the top 1, 010 scientists in computer science worldwide.
A few years ago we asked a small panel of scientific authorities in computer science, in both European and US universities,
while the seminal theoretical contributions to the entire field of computer science were conceived by European thinkers (Alan Turing
it nurtures the ecology of ideas and visions that feed innovation Table 1. Origins of most important ideas in computer science and technology Top ten ideas in computer science 1. Turing machine
Luckily, computer science and the computer industry have been the object of a massive historical literature, that has highlighted several key factors.
This opened the way to a large diffusion of courses in computer science across US universities. Meanwhile, US universities started to be involved in research on the component technologies underlying the computer.
In France the theoretical roots of computer science were laid down as early as the 1930s. The french mathematician Louis Couffignal demonstrated how a programmable binary calculator could be constructed using electromechanical technology as early as 1938,
and developed the notion of bracketed structures, a fundamental breakthrough in computer science, while Bauer was the first to propose the stack method of expression evaluation.
It is clear that the institutionalization of computer science as an academic discipline took place earlier in the USA, approximately in the 1950s,
characterizing the search regime of computer science In a stream of recent papers (Bonaccorsi, 2007; 2008;
It is therefore useful to try to characterize the history of computer science from the point of view of the underlying abstract dynamics of knowledge.
The National Research Council (NRC) of the US National Academies has edited a number of essays from leading scientists on the state of the art of computer science, with a collective introduction (NRC, 2004.
Computer science embraces questions ranging from the properties of electronic devices to the character of human understanding, from individual designer components to globally distributed systems,
Computer science encompasses basic research that seeks fundamental understanding of computational phenomena, as well as applied research. The two are coupled often;
in computer science there is a significant overlap. Great theorists also engage in developing (or have their students develop) software code
computer science research (NRC, 2004: 15: involves symbols and their manipulation and the creation and manipulation of abstractions. creates
On the basis of an extensive historical reconstruction and of informed reports from scientists, we can conclude that the search regime of computer science has been characterized by turbulent We conclude that the search regime of computer science has been characterized by a turbulent rate of growth, proliferation dynamics,
has attracted a large number of other disciplines into computer science, creating powerful forms of cognitive complementarity. Not only mathematics, logics,
and electric and electronic engineering have been involved into computer science since the beginning, but also biology and chemistry (bioinformatics), earth sciences (geographic information systems), psychology (artificial intelligence), visual art (computer graphics), operations management (enterprise resource planning),
All have been transformed deeply from the relationship with computer science. In all cases, there was not just‘application,
In computer science, this complementarity comes from the constitutive interplay between theoretical work and pragmatic goals (Bonaccorsi, 2010.
New evidence on scientific excellence in computer science An analysis of the CVS of top computer scientists An interesting perspective is to look at the large community of computer scientists and at their own self-validation processes.
Citations to papers in computer science are recorded automatically by Citeseer, 2 a highly structured indexing system established in 1997 and endorsed by most scientific societies and departments in computer science worldwide.
The Citeseer service ranks scientists by the total number of citations without checking for homonyms and controlling for the age of scientists.
These scientists have the largest cumulative number of citations in papers from a list of journals and conferences in computer science, irrespective of their age.
it is almost impossible to rank high in the computer science field without a Phd from either the USA or Europe,
In the period 1980 1989, a period of explosion of computer science and information technology, US Table 2. Distribution of degrees of top computer scientists by geographical area Area Phd degree Master degree
457 100.0 641 100.0 It is almost impossible to rank high in the computer science field without a Phd from either the USA or Europe, with the USA leading by a large margin European competitiveness:
and place of Phd degree of top scientists in computer science Year USA Europe Asia Other Not available Total<1950 4 4 0 0 0 8
Master and Bachelor degrees to top scientists in computer science Phd degree Master degree Bachelor degree Number%Number%Number%MIT 82 9. 6 47
when we move to the Bachelor degree, the entry point for students considering a career in computer science.
The talent pool for a career in computer science is worldwide. Entry points are good universities offering strong basic scientific knowledge
With few exceptions, European postgraduate education in computer science is not globally competitive. If it were competitive we would see more students migrating from Asia and the rest of the world into Europe, instead of the USA,
not computer science (see Table 5). The entry point of a scientific career is not in the specialised field,
Also interesting is the group of graduate students in physics who are recognized as key leaders in computer science.
computer science is number one at the level of Master degrees, a stage in which some focusing is required.
Finally, at the Phd stage the disciplinary affiliation of computer science dominates with 38.2%of cases.
because it is considered obvious that their Phd is in computer science?).At the same time an interesting tentative interpretation can be offered.
Computer science is a relatively young discipline. It has not the long scientific history of physics, mathematics, or chemistry.
a Table 5. Distribution of Phd, Master and Bachelor degrees by discipline Phd degree Master degree Bachelor degree Number%Number%Number%Computer science 327 38.2
Our data seem to suggest that computer science has been a gateway for cross-discipline mobility and cognitive recombination.
students with a background in human sciences (literature, linguistics, psychology) and social sciences (economics) may combine their domain expertise with advanced computer science.
This is roughly confirmed for computer science (79.4%on the diagonal cell) but not for mathematics and engineering.
We therefore conclude that computer science is characterized a field by a high degree of disciplinary mobility attracting competences from related fields.
which is Table 6. Transition matrix between disciplinary distribution of Bachelor and Phd degrees Bachelor degree Phd degree Mathematics Engineering Computer science Other disciplines No Phd Total
100.0 Engineering 4 41.8 69 34.5 57 17.6 29 17.6 6 3. 6 165 100.0 Computer science-2. 0 2 79.4
. 8 432 100.0 The search regime of computer science has been characterized by a turbulent rate of growth, proliferation dynamics,
Our data seem to suggest that in the computer sciences the pattern of geographic mobility has been an ingredient of long-term success. Scientific productivity We offer a very rough descriptive analysis of the scientific production of top scientists.
These cover only a subset of journals considered important in the computer science community, and do not include many top conferences,
This confirms the notion that institutional complementarity is an integral part of the search regime in computer science.
combine different disciplines around computer science, enjoy a rapid career, have extensive industry involvement, as witnessed by research collaborations,
Computer science has been based on a fierce competition for students and researchers worldwide. Knowing how severe these demands are,
However, there is also very recent evidence that the type of brain race that we have discovered in the computer science is becoming widespread (Wildavsky
The search regime in computer science is based on a massive and fast effort of exploration of many competing directions,
Computer science. Reflections on the Field, Reflections from the Field. WASHINGTON DC: National Academies Press. Nicoletti, G and S Scarpetta 2003.
an experiment that escaped from the lab. In Computer science. Reflections on the Field, Reflections from the Field, National Research Council (ed.),pp 129 133.
In Computer science. Reflections on the Field, Reflections from the Field, National Research Council (ed.),pp 151 158.
A Century of Electrical engineering and Computer science at MIT, 1882 1982. Cambridge, MA: MIT Press Williams, M R 2000 A preview of things to come:
In Foundations of Computer science. Potential-Theory-Cognition, C Freksa, M Jantzen and R Valk (eds..
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