Characterizing Interdisciplinarity of Researchers and Research Topics Using Web Search Engines

Researchers' networks have been subject to active modeling and analysis. Earlier literature mostly focused on citation or co-authorship networks reconstructed from annotated scientific publication databases, which have several limitations. Recently, general-purpose web search engines have also been utilized to collect information about social networks. Here we reconstructed, using web search engines, a network representing the relatedness of researchers to their peers as well as to various research topics. Relatedness between researchers and research topics was characterized by visibility boost-increase of a researcher's visibility by focusing on a particular topic. It was observed that researchers who had high visibility boosts by the same research topic tended to be close to each other in their network. We calculated correlations between visibility boosts by research topics and researchers' interdisciplinarity at individual level (diversity of topics related to the researcher) and at social level (his/her centrality in the researchers' network). We found that visibility boosts by certain research topics were positively correlated with researchers' individual-level interdisciplinarity despite their negative correlations with the general popularity of researchers. It was also found that visibility boosts by network-related topics had positive correlations with researchers' social-level interdisciplinarity. Research topics' correlations with researchers' individual- and social-level interdisciplinarities were found to be nearly independent from each other. These findings suggest that the notion of "interdisciplinarity" of a researcher should be understood as a multi-dimensional concept that should be evaluated using multiple assessment means.Comment: 20 pages, 7 figures. Accepted for publication in PLoS On

Computer-based methods of knowledge generation in science - What can the computer tell us about the world?

Der Computer hat die wissenschaftliche Praxis in fast allen Disziplinen signifikant verändert. Neben traditionellen Quellen für neue Erkenntnisse wie beispielsweise Beobachtungen, deduktiven Argumenten oder Experimenten, werden nun regelmäßig auch computerbasierte Methoden wie ‚Computersimulationen‘ und ‚Machine Learning‘ als solche Quellen genannt. Dieser Wandel in der Wissenschaft bringt wissenschaftsphilosophische Fragen in Bezug auf diese neuen Methoden mit sich. Eine der naheliegendsten Fragen ist dabei, ob diese neuen Methoden dafür geeignet sind, als Quellen für neue Erkenntnisse zu dienen. Dieser Frage wird in der vorliegenden Arbeit nachgegangen, wobei ein besonderer Fokus auf einem der zentralen Probleme der computerbasierten Methoden liegt: der Opazität. Computerbasierte Methoden werden als opak bezeichnet, wenn der kausale Zusammenhang zwischen Input und Ergebnis nicht nachvollziehbar ist. Zentrale Fragen dieser Arbeit sind, ob Computersimulationen und Machine Learning Algorithmen opak sind, ob die Opazität bei beiden Methoden von der gleichen Natur ist und ob die Opazität verhindert, mit computerbasierten Methoden neue Erkenntnisse zu erlangen. Diese Fragen werden nah an der naturwissenschaftlichen Praxis untersucht; insbesondere die Teilchenphysik und das ATLAS-Experiment am CERN dienen als wichtige Fallbeispiele. Die Arbeit basiert auf fünf Artikeln. In den ersten beiden Artikeln werden Computersimulationen mit zwei anderen Methoden – Experimenten und Argumenten – verglichen, um sie methodologisch einordnen zu können und herauszuarbeiten, welche Herausforderungen beim Erkenntnisgewinn Computersimulationen von den anderen Methoden unterscheiden. Im ersten Artikel werden Computersimulationen und Experimente verglichen. Aufgrund der Vielfalt an Computersimulationen ist es jedoch nicht sinnvoll, einen pauschalen Vergleich mit Experimenten durchzuführen. Es werden verschiedene epistemische Aspekte herausgearbeitet, auf deren Basis der Vergleich je nach Anwendungskontext durchgeführt werden sollte. Im zweiten Artikel wird eine von Claus Beisbart formulierte Position diskutiert, die Computersimulationen als Argumente versteht. Dieser ‚Argument View‘ beschreibt die Funktionsweise von Computersimulationen sehr gut und ermöglicht es damit, Fragen zur Opazität und zum induktiven Charakter von Computersimulationen zu beantworten. Wie mit Computersimulationen neues Wissen erlangt werden kann, kann der Argument View alleine jedoch nicht ausreichend beantworten. Der dritte Artikel beschäftigt sich mit der Rolle von Modellen in der theoretischen Ökologie. Modelle sind zentraler Bestandteil von Computersimulationen und Machine Learning Algorithmen. Die Fragen über die Beziehung von Phänomenen und Modellen, die hier anhand von Beispielen aus der Ökologie betrachtet werden, sind daher für die epistemischen Fragen dieser Arbeit von zentraler Bedeutung. Der vierte Artikel bildet das Bindeglied zwischen den Themen Computersimulation und Machine Learning. In diesem Artikel werden verschiedene Arten von Opazität definiert und Computersimulationen und Machine Learning Algorithmen anhand von Beispielen aus der Teilchenphysik daraufhin untersucht, welche Arten von Opazität jeweils vorhanden sind. Es wird argumentiert, dass Opazität für den Erkenntnisgewinn mithilfe von Computer-simulationen kein prinzipielles Problem darstellt, Model-Opazität jedoch für Machine Learning Algorithmen eine Quelle von fundamentaler Opazität sein könnte. Im fünften Artikel wird dieselbe Terminologie auf den Bereich von Schachcomputern angewandt. Der Vergleich zwischen einem traditionellen Schachcomputer und einem Schachcomputer, der auf einem neuronalen Netz basiert ermöglicht die Illustration der Konsequenzen der unterschiedlichen Opazitäten. Insgesamt ermöglicht die Arbeit eine methodische Einordnung von Computersimulationen und zeigt, dass sich weder mit einem Bezug auf Experimente noch auf Argumente alleine klären lässt, wie Computersimulationen zu neuen Erkenntnissen führen. Eine klare Definition der jeweils vorhanden Opazitäten ermöglicht eine Abgrenzung von den eng verwandten Machine Learning Algorithmen

Recovering the Past: Eastern European Web Mining Platforms for Reconstructing Political Attitudes

During the past half century, the political attitude of the Eastern European people toward the state, government and society changed dramatically. So did their value systems. Inglehart's materialist vs. post-materialist comparative analysis gives a measure of this value change, but not enough as to fully characterize the phenomena underlining the differences in political culture before and after the Fall of Berlin Wall. Little has left from the communist regimes to prove how this change actually occurred and where we are as compared to the stable democratic regimes. With rare exceptions, no public survey has been developed in the Eastern European countries between 1950-1990 able to mirror people's true beliefs and values. In order to understand the current value systems and political attitudes of the people in the Eastern Europe, we have to recover the past. One way to do that is to identify key concepts in the texts, discourses, audio and video recordings of the past times. The present paper provides the rationale of this approach and describes a system which works on dynamically collecting content-based items from library and web references and resources. The system currently works on concepts described by single words or compound expressions, and could be extended so as to work on multimedia items, like words, images, and sounds (voices, music, audio signals, etc.). Our approach aims at constructing a dynamic system and an open access repository of content-based collections of the past and offers a research instrument to the students of political attitudes toward democracy and freedom of the people in Eastern Europe. We approach the problem of recovering the historical process of political change in the Eastern European societies known as the Fall of Berlin Wall in terms of political attitude change modeling and simulation. Modeling makes intensive use of web and data mining technologies for identifying political attitude structural configurations in patterns of value and belief change. Based on web-extracted political attitude configurations, simulation provides a clue on how political attitude structure looks like, and how political attitude change emerges in macro level political change phenomena

Web archives: the future

T his report is structured first, to engage in some speculative thought about the possible futures of the web as an exercise in prom pting us to think about what we need to do now in order to make sure that we can reliably and fruitfully use archives of the w eb in the future. Next, we turn to considering the methods and tools being used to research the live web, as a pointer to the types of things that can be developed to help unde rstand the archived web. Then , we turn to a series of topics and questions that researchers want or may want to address using the archived web. In this final section, we i dentify some of the challenges individuals, organizations, and international bodies can target to increase our ability to explore these topi cs and answer these quest ions. We end the report with some conclusions based on what we have learned from this exercise

Minds Online: The Interface between Web Science, Cognitive Science, and the Philosophy of Mind

Alongside existing research into the social, political and economic impacts of the Web, there is a need to study the Web from a cognitive and epistemic perspective. This is particularly so as new and emerging technologies alter the nature of our interactive engagements with the Web, transforming the extent to which our thoughts and actions are shaped by the online environment. Situated and ecological approaches to cognition are relevant to understanding the cognitive significance of the Web because of the emphasis they place on forces and factors that reside at the level of agent–world interactions. In particular, by adopting a situated or ecological approach to cognition, we are able to assess the significance of the Web from the perspective of research into embodied, extended, embedded, social and collective cognition. The results of this analysis help to reshape the interdisciplinary configuration of Web Science, expanding its theoretical and empirical remit to include the disciplines of both cognitive science and the philosophy of mind

State of the field: digital history

Computing and the use of digital sources and resources is an everyday and essential practice in current academic scholarship. The present article gives a concise overview of approaches and methods within digital historical scholarship, focussing on the question: How have the Digital Humanities evolved and what has that evolution brought to historical scholarship? We begin by discussing techniques in which data are generated and machine searchable, such as OCR/HTR, born-digital archives, computer vision, scholarly editions, and Linked Data. In the second section, we provide examples of how data is made more accessible through quantitative text and network analysis. We close with a section on the need for hermeneutics and data-awareness in digital historical scholarship. The technologies described in this article have had varying degrees of effect on historical scholarship, usually in indirect ways. For example, technologies such as OCR and search engines may not be directly visible in a historical argument; however, these technologies do shape how historians interact with sources and whether sources can be accessed at all. It is with this article that we aim to start to take stock of the digital approaches and methods used in historical scholarship which may serve as starting points for scholars to understand the digital turn in the field and how and when to implement such approaches in their work