Thomas Thiele

Enhancing Scientific Cooperation of an Interdisciplinary Cluster of Excellence via a Scientific Cooperation Portal

In the Cluster of Excellence (CoE) “Integrative Production Technology for High-Wage countries” at RWTH Aachen University, scientists from different institutions investigate interdisciplinary ways to solve the polylemma’s tradeoffs between scale and scope as well as between plan and value oriented production. Next to the CoE’s four scientific subfields – the Integrative Cluster Domains (ICDs) – there are three additional subprojects performing cross sectional research and providing means for physical and virtual cross-linkage, the Cross Sectional Processes (CSP). Scientific cooperation in such a large and diverse consortium – as a meta-structure to the structures present in the member institutes – poses many challenges. To tackle these, an online learning and collaboration platform is developed, called the “Scientific Cooperation Portal”, to optimize the cluster-wide cooperation process. Technically building on the Liferay framework, the portal provides basic features like a member list and an event calendar as well as functionalities to help cluster members to gain a deeper understanding of the CoE’s current state regarding the diversity in interdisciplinary terminology, patterns in publication relationships, knowledge management and developed technologies.

Exploring Research Networks with Data Science: A Data-Driven Microservice Architecture for Synergy Detection

The determination of synergies in research networks is often approached by citation and co-authorship analysis using scientific publications. Although the latter methods allow a detailed insight into scientific cooperation and communities, the major part of the data, the text body of the publication, is mostly ignored. Hence, this text body contains valuable data, which can be processed in order to describe entities in a research network, detect synergies between those entities and make use of these synergies, e.g. in further cooperation. This paper aims at the description of a prototypic architecture, which uses data science to detect hidden topics from publication data. These topics are used to describe entities (e.g. institutes or projects) in a research network. Topical proximity between those entities is computed by the application of classification. In order to enable actors to explore the results of these processes, the architecture automatically generates graph-based visualizations of the derived synergies. This paper contains a description and definition of the technical elements and the process chain of the architecture to derive these synergies in network data. This is accompanied by a short outline of results with the prototype.

Scientific Cooperation Engineering Making Interdisciplinary Knowledge Available within Research Facilities and to External Stakeholders

In this paper we introduce the Scientific Cooperation Portal (SCP), a social enterprise software, and how it is integrated into our process of Scientific Cooperation Engineering. This process is applied in a large-scale interdisciplinary research cluster to ensure and manage the success of the interdisciplinary cooperation of over 180 researchers in different qualification levels. We investigate the influence of shared method competencies as an exemplary driver for collaboration. From the results we address both offline and online measures to improve interdisciplinary collaboration. We show how the knowledge generated from offline measures such as colloquia are transferred to the SCP and connected with other data available on the portal. This includes the handling of interdisciplinary terminologies, the disposability of publications and technology data sheets. The portal fosters knowledge exchange, and interdisciplinary awareness within the research cluster as well as technology dissemination both within the cluster, across the university, and into industry. The effectiveness of the approach is continuously assessed using a traditional balanced scorecard approach as well as additional qualitative measures such as interviews and focus groups.

Unterstützung interdisziplinärer Integration am Beispiel einer Exzellenzcluster-Community

Aktuelle wissenschaftliche Fragestellungen sind haufig nur durch eine Vielzahl unterschiedlicher wissenschaftlicher Fachdisziplinen zu beantworten. Dies gilt besonders fur Forschung im Bereich des Maschinenbaus und speziell im Bereich der Produktionstechnik. Zielsetzung ist dabei die Kombination verschiedener Kompetenzen aus unterschiedlichen Fachdisziplinen zur Losung von Forschungsfragen und Problemstellungen einzusetzen, die sich an Schnittstellen der Fachdisziplinen ergeben. Voraussetzung hierfur ist die Integration dieser verschiedenen Fachdisziplinen in einer interdisziplinaren Community. Im Rahmen dieses Beitrags wird zunachst ein Analyseinstrument vorgestellt, dass zur Erfassung von Bedarfen zur Integration innerhalb der interdisziplinaren Community dient. Drei Masnahmen zur Unterstutzung dieser Integration werden vorgestellt, deren Entwicklung auf Basis von Bedarfen der Community begonnen wurde und auf dieser kontinuierlich aktualisierten Grundlage iterativ fortgefuhrt wird. Das Ziel dieses Vorgehens ist die Unterstutzung einer kontinuierlich optimierten Integration der Community im Exzellenzcluster ,,Integrative Produktionstechnik fur Hochlohnlander“.

Human-in-the-Loop Processes as Enabler for Data Analytics in Digitalized Organizations

As a key driver for innovation in science, economy and society, digitalization affects almost every aspect of our daily working and living environments. The opportunity to track data about processes, persons, and other entities in organizations allows new opportunities for digitalized working scenarios and the creation of new perspectives on matters such as interand intra-organizational relationships. The aim of this paper is to elaborate on these perspectives on the basis of studies that are currently a part of our research activities. Firstly, a framework is outlined that combines topic modeling of textual data and machine learning to derive thematic synergies in the data, for example, between organizations or research projects. Secondly, classical benchmarking approaches are extended by developing a suitable text-mining process for interdisciplinary research. Lastly, a brief concept about evolution as a method for further optimizations and its implications for the human-in-the-loop process is outlined. Altogether, the approaches contribute to a comprehensive human-in-the-loop model – defined as a model that combines intelligent data technologies with human interaction – in the culture of innovation amongst modern, highly digitalized organizations.

Text Mining Analytics as a Method of Benchmarking Interdisciplinary Research Collaboration

This paper introduces the process of adopting and implementing modern text mining approaches of analysis within the Cluster of Excellence (CoE) Tailor-Made Fuels from Biomass (TMFB) at RWTH Aachen University and presents initial results of the analysis of research output by use of common clustering algorithms, namely Principal Component Analysis and k-means. As one main part of this paper the data driven approach is classified into benchmarking efforts, which are part of the research work of the so called Supplementary Cluster Activities. The SCA, supporting the cluster management, are initiated in order to promote interdisciplinary collaboration of CoE researchers with different disciplinary backgrounds. This cross-linking is aided by means of knowledge engineering and knowledge transfer strategies, such as the exploration of synergies and benchmarking of research results as well as progress. In this course an adoption of current benchmarking efforts to the specific cluster research framework conditions is described. At this, in case of differing data sources according to those used in widespread business organisational benchmarking, possible TMFB data sources are outlined and a selection for analysis is reasoned. While benchmarking is usually differentiated in internal and external benchmarking, in this case focus lies on internal analysis of publications in order to reflect research work. Benchmarking of publications is used and implemented to identify (best) methods, practices and processes of CoE to improve the research organization. Second major part and central question within the scope of this paper is in which way text mining respectively clustering algorithms are sensitive applicable to TMFB publications and are able to be used as benchmark for research clusters. Thus thematically priorities of TMFB researchers will be investigated in order to create an overview according to research topics, keywords and methods. In case of an outlook further steps, e. g. dealing with generated results, data visualisation or further acquisition of data corpora, are formulated.

Managing interdisciplinary research clusters

The complexity and dynamicity of interdisciplinary research clusters requires an efficient management in order to ensure a good performance. Therefore, this paper presents an iterative regulatory process for managing interdisciplinary research clusters which has been implemented at the two clusters of excellence at RWTH Aachen University. Thereby, an annual evaluation of the cluster performance through a cluster-specific employee survey forms the basis for the derivation and implementation of different measures. By evaluating the performance again, the loop of organizational learning starts anew. As one example of these measures, the colloquia of employees are described in further details because the adaptation and optimization of concepts illustrate the effects of the continuous improvement. Furthermore, the general performance development of the cluster was analyzed to show the comprehensive effects of the regulatory process.

Digitalisierung der Arbeit und demografischer Wandel

Die Arbeitsforschung hat sich in den letzten funfzehn Jahren intensiv mit dem Thema des demografischen Wandels befasst. Nun deutet sich ein neuer Megatrend an, der in das Zentrum der Arbeitsforschung geraten wird: Die zunehmende Digitalisierung der Arbeit, die mit dem Schlagwort Industrie 4.0 beschrieben wird. Personen und Unternehmen werden sich mit beiden Entwicklungen gleichermasen auseinandersetzen und Gestaltungsfahigkeit fur die sich weiter dynamisch verandernden Arbeits- und Lebenswelten entwickeln mussen. Die Aufgabe der Arbeitsforschung liegt dabei in der Erarbeitung von Strategien, Konzepten und Modellen, damit Menschen und Unternehmen die Entwicklungspotenziale der Digitalisierung der Arbeit nutzen und gleichzeitig lernen, mit den zunehmenden Ambivalenzen der Entwicklung umzugehen.

Integrative Knowledge Management in Interdisciplinary Research Clusters

Interdisciplinary Research (IDR) is described as a specific mode of collaboration: Besides the clash of different institutional cultures (e.g. different expectations/working processes), there is a clash of epistemic cultures (i.e. styles of thinking, different languages) (Joos, C. (2014): Interdisciplinary Research collaboration: Critical Incidents from the researchers’ perspective, in Gestaltung von Kooperationsprozessen interdisziplinarer Forschungsnetzwerke, Norderstedt: BOD-Verlag, pp. 146–179). Former research shows that the involved researchers demand an integrative knowledge management to support the expected integration of cultures (ibid.). In this paper two major aspects regarding integrative knowledge management for IDR are discussed. On the one hand the need for integrative knowledge management based on the researchers’ perspective is depicted in the context of a use case. On the other hand the concept of a virtual mean is elaborated, which supports these needs. Both aspects underline the importance of a process-accompanying support in close coordination with the respective needs of the involved researchers.

Detecting Central Research Results in Research Alliances Through Text Mining on Publications

Not only in the German research landscape, the establishment of research alliances has become a key element of the national funding structure, especially in order to address current societal, economic and scientific problems. These complex problems are mutually investigated by heterogeneous actors whose heterogeneity can be mainly seen in a combined research effort of scientific as well as business-driven research – a so-called transdisciplinary research approach. The main challenge which arises from this approach covers the cooperation of numerous actors in a complex and often intransparent collaboration structure. To allow transparence of the central research topics within these structures, publication data has to be consolidated and classified.