Kohavi et al[1] is an extremely useful survey and guide to controlled experiments on/using the web, told primarily from a marketing perspective. It introduces and describes various experimental methods, examines the technical and organization challenges of running controlled experiments, and delves into various issues of experimental design. It is - for the most part - an excellent resource for anyone wanting to do these kinds of web-based controlled experiments.
While I know this article is marketing-oriented, it is clear that some of the results from these experiments will be/have been published in peer-reviewed journals. Yet the authors make no mention of informed consent - even as an aside - in the entire article (and no mention of privacy or privacy issues either). Some of the experiments described or cited are not too different from those that might be done in social sciences or IT user interface research, where researchers are usually required to go through an ethics review process and invariable need to obtain informed consent from their subjects.
It seems that you just need to say it is for marketing and these issues all go away.
[1]R. Kohavi, R. Longbotham, D. Sommerfield & R. Henne. 2009. Controlled experiments on the web: survey and practical guide. Data Mining and Knowledge Discovery 18:1:140-181. http://dx.doi.org/10.1007/s10618-008-0114-1
Monday, December 22, 2008
Web (marketing) controlled experiments == No informed consent?
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Glen Newton
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11:29
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Labels: data mining, ethics, ethics review board, infromed consent, knowledge discovery, privacy, publicly funded research, web research
Tuesday, December 09, 2008
Open Standards and standards organizations
This report - from January 2008 - examines 10 "open" standards organizations and evaluates how "open" they are. It uses a methodology that maps directly into Krechmer's open standards requirements.
The organizations reviewed are:
- CEN (European Committee for Standardization)
- Ecma (European association for standardizing information and communication systems
- ETSI (European Telecommunications Standards Institute)
- IETF (Internet Engineering Task Force)
- ISO (International Organization for Standardization)
- ITU (International Telecommunication Union)
- NIST (National Institute of Technology and Standards)
- OASIS (Organization for the Advancement of Structured Information Standards)
- OMG (Object Management Group)
- W3C (World Wide Web Consortium)
Evaluation of Ten Standard Setting Orgizanizations with Regard to Open Standards
Abstract: On 2 June 2006, the Danish parliament (the Folketing) unanimously adopted
Parliamentary Resolution B103 on the use of open standards for software in the
public sector. The Resolution instructs the Government to ensure that the
public sector's use of information technology, including the use of software,
should be based on open standards. Therefore, the Danish National IT and
Telecom Agency (IT- og Telestyrelsen) has commissioned to IDC to evaluate the
degree of "openness" of the leading standard setting organizations.
See also:
- ODF made national standard in Sweden (Sept 2008)
- Denmark: Committee appointed to evaluate impact of Open Standards June 2008
- Double Standards? Trial Mandation of Dual Standards (June 2007)
- Denmark Says ODF and Open XML Ok (June 2007)
- Denmark's Resolution on Open Standards - Updated (Groklaw, June 2006)
- Open geospatial standards in local governments in Denmark
Posted by
Glen Newton
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12:28
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Labels: CEN, emca, ETSI, IETF, ISO, ITU, NIST, OASIS, OMG, open standards, standards organizations, W3C
Friday, December 05, 2008
Article: Canadian Federal Support for University Research Commercialization
Rasmussen[1] does a thorough examination of Canadian federal government programs and organizations supporting the commercialization of university research. This work is based on background research and interviews in January 2006 with 28 "...policy makers, program managers, policy researchers, university administrators, and program users..", including "A case description was written based on the collected material and later verified by several key people at Canadian agencies".
For those following federal university commercialization activities, this work is an excellent review of the recent state of these programs, activities and organizations.
It should be noted that this research is part of a larger and broader research effort[2] benchmarking commercialization of research in Canada, Finland, Ireland, the Netherlands, Scotland, and Sweden. The list of the Canadian interviewees can be found in this larger work (p.52).
Programs:
"Compared to most countries, Canada has a long tradition of state involvement to promote the economic utilization of scientific research (Atkinson-Grosjean et al., 2001; Slaughter and Leslie, 1997). Moreover, Canada has an overwhelming number of programs at federal and provincial level that may be used to support the commercialization of research. Although using a very broad definition, one survey identified 178 initiatives that represented an expenditure of Canadian dollar (CAD) 3.2 billion a year (Gault and McDaniel, 2004)."
Efficiency of university commercialization:
"Clayman (2004) found that Canadian universities created considerably more spin-off companies than their US counterparts, counting the companies created per dollar of research."
R&D Expenditure: Private/public:
"Canada has a relatively modest level of R&D expenditure due to low investments in the private sector. Public R&D expenditure is, however, among the highest in the world. About one-third of all R&D activity is performed by Canada's close to 100 universities and university colleges (most by the top 20), roughly 12% by government institutes, and just above half by Canadian industry."
Diversity of IP Policy at Canadian universities:
"It is also important to note that Canadian universities have a diversity of approaches to IP ownership, IP strategies, and the organization of their technology transfer activities. For instance, in the city of Vancouver the University of British Columbia owns the IP, while at Simon Fraser University the IP is owned by the inventors. Among the 20 largest universities, the IP is owned by the creator (academics) in eight cases, in another eight cases the IP is university owned, and the remaining four have joint ownership or case-by-case negotiations."
Three Categories of Commercialization Initiatives:
"Federal level initiatives to support the commercialization of Canadian research could be divided into three agency areas. First, the federal research institutes such as NRC make their own internal priorities in supporting commercialization. Second, there are a number of targeted schemes from CIHR, NSERC, and SSHRC towards commercialization at universities. Third, general agencies such as the Industrial Research Assistance Program (NRC-IRAP) and the Business Development Bank of Canada (BDC) give considerable support to research-based spin-off firms. For instance, about half the Canadian university spin-offs have received IRAP funds, and 23 of 35 investments by BDC's Technology Seed Investments involved spin-offs from universities or federal labs according to officials in these organizations."
Approach: bottom-up
"Although all the initiatives investigated in this study are operated by government agencies, they seem to emphasize a bottom-up approach (Goldfarb and Henrekson, 2002). That is, to be flexible according to local needs and support with funding, expertise development, experimenting, and networks, in contrast to a top-down approach imposing a general set of policies and structures for the commercialization of research. As argued by Goldfarb and Henrekson, 2002, a bottom-up approach is a key explanation for the success at US universities in promoting commercialization of research, in contrast to the limited success of the top-down approach in Sweden."
Metrics: People and cooperation would be better?
"A final observation related to the commercialization of university research is that the use of quantitative measures (number of patents, licenses, spin-off firms, revenue generated, etc.) to measure the outcome of technology transfer activity is increasingly critiqued in Canada (Langford et al., 2006). It is recognized that the major channels for technology transfer are the transfer of people, especially graduated students, and research cooperation with existing industry, including faculty consulting. Hence, licensing and spin-offs account for only a small share of technology transfer from research institutions and their impact might be difficult to separate from the other technology transfer activity (Landry et al., 2007). Several Canadian officials expressed concern that a too narrow focus on short-term indicators could be misinterpreted and do more harm than good in order to achieve the potential for social and economic benefits from research."
Programs/Organizations/Activities examined in some detail in this article:
- Intellectual Property Mobilization program (IPM)
- NSERC College and Community Innovation Pilot Program
- CIHR Proof of Principle Program (POP)
- NSERC Idea to Innovation (I2I) program
- NRC-Industrial Research Assistance Program (IRAP)
- Department of Industry Commercialization pilot
- Flintbox
References
[1]Einar Rasmussen. 2008. Government instruments to support the commercialization of university research: Lessons from Canada. Technovation 28:8:506-517. http://dx.doi.org/10.1016/j.technovation.2007.12.002.Related:
[2]Einar Rasmussen, Odd Jarl Borch, Roger Sørheim, Are Gjellan. 2006. Government initiatives to support the commercialization of research - an international benchmarking study.
- Moira Decter,David Bennett,Michel Leseure. 2007. University to business technology transfer: UK and USA comparisons. Technovation 27:3:145-155. http://dx.doi.org/10.1016/j.technovation.2006.02.001
- Timothy R. Anderson, U. Daim, Francois F. Lavoie. 2007. Measuring the efficiency of university technology transfer. Technovation 27:5:306-318.
http://dx.doi.org/10.1016/j.technovation.2006.10.003 - P. Craig Boardman. 2008. Beyond the stars: The impact of affiliation with university biotechnology centers on the industrial involvement of university scientists. Technovation 28:5:291-297, http://dx.doi.org/10.1016/j.technovation.2007.06.001
- Beth Young, Nola Hewitt-Dundas, Stephen Roper. 2008. Intellectual Property management in publicly funded R&D centres: A comparison of university-based and company-based research centres. Technovation 28:8:473-484. http://dx.doi.org/10.1016/j.technovation.2008.02.004
Dislaimer/disclosure: I am employed by the National Research Council, some of whose activities are described in the above articles. This is a personal blog whose content is my own opinion and does not reflect the policies, views or opinions of the NRC or the Government of Canada.
Posted by
Glen Newton
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15:49
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Labels: Canada, federal, technology transfer, universities
Uncertainty Reasoning for the Semantic Web I
Uncertainty Reasoning for the Semantic Web I, ISWC International Workshops, URSW 2005-2007, Revised Selected and Invited Papers. DOI http://dx.doi.org/10.1007/978-3-540-89765-1, Lecture Notes in Computer Science.
Of note:
- Towards Machine Learning on the Semantic Web. http://dx.doi.org/10.1007/978-3-540-89765-1_17
Author copy: http://www.cs.ubc.ca/spider/poole/papers/SemSciChapter2008.pdf - Semantic Science: Ontologies, Data and Probabilistic Theories. http://dx.doi.org/10.1007/978-3-540-89765-1_2
- Analogical Reasoning in Description Logics. http://dx.doi.org/10.1007/978-3-540-89765-1_19
- Fernando Bobillo, Miguel Delgado, Juan Gómez-Romero. 2008. A Crisp Representation for Fuzzy with Fuzzy Nominals and General Concept Inclusions.
http://dx.doi.org/10.1007/978-3-540-89765-1_11 - Mauro Mazzieri, Aldo Franco Dragoni. 2008. A Fuzzy Semantics for the Resource Description Framework.
http://dx.doi.org/10.1007/978-3-540-89765-1_15 - Matthias Nickles, Ruth Cobos. 2008. An Approach to Description Logic with Support for Propositional Attitudes and Belief Fusion.
http://dx.doi.org/10.1007/978-3-540-89765-1_8 - Andrea Calì, Thomas Lukasiewicz. 2008. An Approach to Probabilistic Data Integration for the Semantic Web.
http://dx.doi.org/10.1007/978-3-540-89765-1_4 - Hai-Tao Zheng, Bo-Yeong Kang, Hong-Gee Kim. 2008. An Ontology-Based Bayesian Network Approach for Representing Uncertainty in Clinical Practice Guidelines.
http://dx.doi.org/10.1007/978-3-540-89765-1_10 - Claudia d'Amato, Nicola Fanizzi, Floriana Esposito. 2008. Analogical Reasoning in Description Logics.
http://dx.doi.org/10.1007/978-3-540-89765-1_19 - Nicola Fanizzi, Claudia d'Amato, Floriana Esposito. 2008. Approximate Measures of Semantic Dissimilarity under Uncertainty.
http://dx.doi.org/10.1007/978-3-540-89765-1_20 - Francisco Martín-Recuerda, Dave Robertson. 2008. Discovery and Uncertainty in Semantic Web Services.
http://dx.doi.org/10.1007/978-3-540-89765-1_7 - Trevor P. Martin, Yun Shen, Ben Azvine. 2008. Granular Association Rules for Multiple Taxonomies: A Mass Assignment Approach.
http://dx.doi.org/10.1007/978-3-540-89765-1_14 - Pedro Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla. 2008. Just Add Weights: Markov Logic for the Semantic Web.
http://dx.doi.org/10.1007/978-3-540-89765-1_1 - Peter Haase, Johanna Völker. 2008. Ontology Learning and Reasoning: Dealing with Uncertainty and Inconsistency.
http://dx.doi.org/10.1007/978-3-540-89765-1_21 - Fernando Bobillo, Miguel Delgado, Juan Gómez-Romero. 2008. Optimizing the Crisp Representation of the Fuzzy Description Logic.
http://dx.doi.org/10.1007/978-3-540-89765-1_12 - Paulo Cesar G. da Costa, Kathryn B. Laskey, Kenneth J. Laskey. 2008. PR-OWL: A Bayesian Ontology Language for the Semantic Web.
http://dx.doi.org/10.1007/978-3-540-89765-1_6 - Paolo Besana, Dave Robertson. 2008.Probabilistic Dialogue Models for Dynamic Ontology Mapping.
http://dx.doi.org/10.1007/978-3-540-89765-1_3 - Giorgos Stoilos, Giorgos Stamou, Jeff Z. Pan, Nick Simou, Vassilis Tzouvaras. 2008. Reasoning with the Fuzzy Description Logic f-
http://dx.doi.org/10.1007/978-3-540-89765-1_16 - Andrea Calì, Thomas Lukasiewicz, Livia Predoiu, Heiner Stuckenschmidt. 2008. Rule-Based Approaches for Representing Probabilistic Ontology Mappings
http://dx.doi.org/10.1007/978-3-540-89765-1_5 - David Poole, Clinton Smyth, Rita Sharma. 2008. Semantic Science: Ontologies, Data and Probabilistic Theories.
Author copy: http://www.cs.ubc.ca/spider/poole/papers/SemSciChapter2008.pdf
http://dx.doi.org/10.1007/978-3-540-89765-1_2 - Towards Machine Learning on the Semantic Web. 2008. Volker Tresp, Markus Bundschus, Achim Rettinger, Yi Huang.
http://dx.doi.org/10.1007/978-3-540-89765-1_17 - Alan Eckhardt, Tomás Horváth, Duan Marák, Róbert Novotný, Peter
Vojtá. 2008. Uncertainty Issues and Algorithms in Automating Process Connecting Web and User.
http://dx.doi.org/10.1007/978-3-540-89765-1_13 - Volker Haarslev, Hsueh-Ieng Pai, Nematollaah Shiri. 2008. Uncertainty Reasoning for Ontologies with General TBoxes in Description Logic.
http://dx.doi.org/10.1007/978-3-540-89765-1_22 - Joaquín Borrego-Díaz, Antonia M. Chávez-González. 2008. Using Cognitive Entropy to Manage Uncertain Concepts in Formal Ontologies.
http://dx.doi.org/10.1007/978-3-540-89765-1_18
Posted by
Glen Newton
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07:58
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Labels: bayesian logic, descriptive logic, discovery, machine learning, Ontologies, ontology, reasoning, semantic web, uncertainty

