Innovation Systems as Patent Networks: The Netherlands, India and Nanotech

Research in the domain of 'Innovation Studies' has been claimed to allow for the study of how technology will develop in the future. Some suggest that the National and Sectoral Innovation Systems literature has become bogged down, however, into case studies of how specific institutions affect innovation in a specific country. A useful notion for policy makers in particular, Balzat & Hanusch (2004) argued that there is a need for NIS studies to develop complementary and also quantitative methods in order to generate new insights that are comparable across national borders. We use data for patents granted by the World Intellectual Property Organization (WIPO) to map innovation systems. Groupings of patents into primary and secondary classes (co-classification) can be used as relational indicators. Knowledge from one class may be more easily used in another class when a co-classification relation exists. Using social network analysis, we map the co-classification of patents among classes and thus indicate what characterizes an innovation system. A main contribution of this paper is methodological, adding to the repertoire of methods NIS studies use and using information from patents in a different way. Policy makers may also find benefits in the social network analysis of the complete set of patents granted by the WIPO to firms and individuals in a country. Social network analysis indicates what innovation activity occurs in a countries and which fields of technology are likely to give rise to innovative products in the near future. We offer such analysis for the Dutch and Indian Innovation Systems. This social network analysis could also be done for a Sector Innovation System, and we do so for Nanotech to determine empirically the knowledge field relevant for this emerging scientific domain

Performance works: Continuing to comprehend instantiation

Much work in knowledge organization (KO) is conceptual, which results in a theoretical framework that is itself largely conceptual. In some cases empirical methods have been employed as well for direct observation of phenomena. Direct observation provides a critical base point and a variety of empirical approaches have been used to good effect in KO. The phenomenon of instantiation has been examined to date almost entirely based on the analysis of data derived from empirical analysis. In the present paper we demonstrate the efficacy of the empirical model for category generation by taking one category of instantiation—the performance work—and submitting it to analytical scrutiny. Data from three analytical studies are reviewed and placed alongside evidence from datasets gathered for prior studies on instantiation. A performance work is realized in space and time, and thus exists spatiotemporally. The performance work might be derived from a precedent work, related to other works that are embedded, have adjunct documentation, and be accompanied by antecedent works. A naïve classification is derived empirically, a model follows rationally, and together with semiotic elements a partial typology is generated that represents the essential knowledge elements from which a KO schema for performance works might evolve

More than Physique or Desire: Feeling Constituting Identity in Homosexual Nomenclature

This is a preliminary report of work by a Domain Analysis Clinic formed in 2021 to examine homosexual nomenclatures. Prior studies have suggested that self-naming and self-classification in the domain of male gayness and alternative sexualities emerge as a form of resistance against the hegemonic. So, the postulated research question is: what are the reasons, characteristics, and consequences of this type of knowledge organization using as an example the self-representation of gay men in social interaction applications? Our principal methodology was to seek self-identifying nomenclature from social media websites, from each of which we gathered sets of categories or labels used for identifying content uploaded by members. As a form of preliminary analysis all of the data were sorted as keywords or phrases to generate frequency distributions, which can be compared, to some extent, across the sites. Analysis of the terms suggested three classes which also can be considered as facets: sexual desires, physical characteristics and sexual roles or performances. The terms demonstrate how users understand themselves in their individuality, aligning themselves with the social reproduction that occurs in the analyzed social network. The present study corresponds to a first approximation to the development of a classification of male homosexuality, following a pragmatist or domain analysis approach

Knowledge Organization Research in the last two decades: 1988-2008

We apply an automatic topic mapping system to records of publications in knowledge organization published between 1988-2008. The data was collected from journals publishing articles in the KO field from Web of Science database (WoS). The results showed that while topics in the first decade (1988-1997) were more traditional, the second decade (1998-2008) was marked by a more technological orientation and by the appearance of more specialized topics driven by the pervasiveness of the Web environment

The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps

COVID-19: Symptoms Clustering and Severity Classification Using Machine Learning Approach

COVID-19 is an extremely contagious illness that causes illnesses varying from either the common cold to more chronic illnesses or even death. The constant mutation of a new variant of COVID-19 makes it important to identify the symptom of COVID-19 in order to contain the infection. The use of clustering and classification in machine learning is in mainstream use in different aspects of research, especially in recent years to generate useful knowledge on COVID-19 outbreak. Many researchers have shared their COVID-19 data on public database and a lot of studies have been carried out. However, the merit of the dataset is unknown and analysis need to be carried by the researchers to check on its reliability. The dataset that is used in this work was sourced from the Kaggle website. The data was obtained through a survey collected from participants of various gender and age who had been to at least ten countries. There are four levels of severity based on the COVID-19 symptom, which was developed in accordance to World Health Organization (WHO) and the Indian Ministry of Health and Family Welfare recommendations.  This paper presented an inquiry on the dataset utilising supervised and unsupervised machine learning approaches in order to better comprehend the dataset. In this study, the analysis of the severity group based on the COVID-19 symptoms using supervised learning techniques employed a total of seven classifiers, namely the K-NN, Linear SVM, Naive Bayes, Decision Tree (J48), Ada Boost, Bagging, and Stacking. For the unsupervised learning techniques, the clustering algorithm utilized in this work are Simple K-Means and Expectation-Maximization. From the result obtained from both supervised and unsupervised learning techniques, we observed that the result analysis yielded relatively poor classification and clustering results. The findings for the dataset analysed in this study do not appear to be providing the correct result for the symptoms categorized against the severity level which raises concerns about the validity and reliability of the dataset

COVID-19: Symptoms Clustering and Severity Classification Using Machine Learning Approach

COVID-19 is an extremely contagious illness that causes illnesses varying from either the common cold to more chronic illnesses or even death. The constant mutation of a new variant of COVID-19 makes it important to identify the symptom of COVID-19 in order to contain the infection. The use of clustering and classification in machine learning is in mainstream use in different aspects of research, especially in recent years to generate useful knowledge on COVID-19 outbreak. Many researchers have shared their COVID-19 data on public database and a lot of studies have been carried out. However, the merit of the dataset is unknown and analysis need to be carried by the researchers to check on its reliability. The dataset that is used in this work was sourced from the Kaggle website. The data was obtained through a survey collected from participants of various gender and age who had been to at least ten countries. There are four levels of severity based on the COVID-19 symptom, which was developed in accordance to World Health Organization (WHO) and the Indian Ministry of Health and Family Welfare recommendations.  This paper presented an inquiry on the dataset utilising supervised and unsupervised machine learning approaches in order to better comprehend the dataset. In this study, the analysis of the severity group based on the COVID-19 symptoms using supervised learning techniques employed a total of seven classifiers, namely the K-NN, Linear SVM, Naive Bayes, Decision Tree (J48), Ada Boost, Bagging, and Stacking. For the unsupervised learning techniques, the clustering algorithm utilized in this work are Simple K-Means and Expectation-Maximization. From the result obtained from both supervised and unsupervised learning techniques, we observed that the result analysis yielded relatively poor classification and clustering results. The findings for the dataset analysed in this study do not appear to be providing the correct result for the symptoms categorized against the severity level which raises concerns about the validity and reliability of the dataset

Statistical Analysis of Humanities and Social Sciences Collaboration Research in China

With the development of humanities and social sciences, research collaboration becomes more and more important. This article studies collaboration phenomena of seventeen kinds of journals’ from 1995-2004 in china. According to statistical data, some characteristics are disclosed, and some tested explanations are given. This article makes a comparison of research collaboration between China and other country, and some differences are studied. A lot of differences of research collaboration among humanities sciences, social sciences and natural sciences are also pointed out

Evolutionary Subject Tagging in the Humanities; Supporting Discovery and Examination in Digital Cultural Landscapes

In this paper, the authors attempt to identify problematic issues for subject tagging in the humanities, particularly those associated with information objects in digital formats. In the third major section, the authors identify a number of assumptions that lie behind the current practice of subject classification that we think should be challenged. We move then to propose features of classification systems that could increase their effectiveness. These emerged as recurrent themes in many of the conversations with scholars, consultants, and colleagues. Finally, we suggest next steps that we believe will help scholars and librarians develop better subject classification systems to support research in the humanities.NEH Office of Digital Humanities: Digital Humanities Start-Up Grant (HD-51166-10