Event Calendar

JFMH 2024 greift die Debatte um Künstliche Intelligenz (KI) in der Bildung auf. Ziel ist es, über technologiezentrierte Sichtweisen hinauszugehen. Der Schwerpunkt liegt auf der Analyse und Gestaltung von Lernkulturen in der Hochschulbildung, der Erforschung des Potenzials von Digitalisierung und KI sowie der Bewältigung praktischer Herausforderungen. Die Veranstaltung lädt zu Beiträgen über organisatorische Strategien, praktische Lehrmethoden und individuelle Faktoren ein, die Lernerfahrungen in digitalen Umgebungen beeinflussen. Ziel ist es, ein besseres Verständnis dafür zu entwickeln, wie KI Bildungspraktiken und Lernkulturen positiv beeinflussen kann.

Ziel des Workshops ist die Diskussion aktueller Fragen rund um die Entwicklung eines disziplinübergreifenden Referenzmodells für die Erfassung von Forschungsdaten im Kontext von Forschungsinformationen und der wissenschaftlichen Nachvollziehbarkeit von veröffentlichten Forschungsergebnissen, mit dem spezifischen Anwendungsfall der Versionierung von Forschungsdaten.

Citizen science includes different research practices conducted with non-researcher citizen. It makes part of the ppen science movement as a way to bring research processes closer to society by citizen participation. This session will cover different types of citizen science practices.

Get ready for our next paper in economics and business studies! We will show you how to avoid unintentional plagiarism in a short 30-minute-session.

Scholarly Information Extraction’ involves identifying resources, concepts, actors, and their relationships from scholarly documents and related data sources, such as software repositories. This forms the basis for many use cases, including automated literature reviews and advanced search applications. The talk presents the fundamental concepts and methodologies for scholarly information extraction, followed by a presentation of a scholarly information extraction project from start to finish.Scholarly Information Extraction’ involves identifying resources, concepts, actors, and their relationships from scholarly documents and related data sources, such as software repositories. This forms the basis for many use cases, including automated literature reviews and advanced search applications. The talk presents the fundamental concepts and methodologies for scholarly information extraction, followed by a presentation of a scholarly information extraction project from start to finish.

Data Science for Social Good (DSSG) is a two-month summer program where two teams of fellows collaborate on real-world data science projects, aiming to create a meaningful impact for the social good.

In dieser Veranstaltung lernen Sie die Merkmale offener Bildungsmaterialien kennen und reflektieren deren Chancen und Herausforderungen. Besonderes Augenmerk liegt auf dem Urheberrecht und der offenen Lizenzierung. In interaktiven Übungen erfahren Sie, wie Sie offen lizenzierte Bildungsmaterialien in Ihre Lehre einbinden können und was bei der Erstellung eigener OER zu beachten ist. Gemeinsam nehmen wir didaktische Fragen rund um OER in den Blick und diskutieren Qualitätsdimensionen offener Bildungsmaterialien

The Barcamp Open Science is a barcamp dedicated to the Open Science movement. It is open to everybody interested in connecting with like-minded people embracing Open Science, unlocking new perspectives and networking on Open Science, and thriving Open Science together!

The course targets researchers who are interested in understanding, conducting, and publishing meta-analytic research. Participants of this course will learn how to conduct and publish a high-quality meta-analysis in the area of management and business research. To this aim, the course follows a step-by-step procedure that covers the entire meta-analysis research process, including problem formulation and definition of a research question for a meta-analysis, literature search, study and effects coding, data preparation and analysis with different software tools, and reporting and publishing. Participants will further learn how to evaluate meta-analyses in the business and management literature and to follow the respective methodological discussion about meta-analyses in their field.

Im Mittelpunkt der Tagung stehen facettenreiche Diskussionen und Erkenntnisse zu zentralen Themen wie dem Einsatz von Forschungssoftware, der Integration von Forschungsdatenmanagement in Lehre und Teamstrukturen sowie Datennachnutzung und Barrierefreiheit im Forschungskontext

The course exposes participants to recent developments in the field of machine learning and discusses their ramifications for business and economics. Machine learning comprises theories, concepts, and algorithms to infer patterns from observational data. The prevalence of data (“big data”) has led to an increasing interest in the corresponding methodology to leverage existing data assets for improved decision-making and business process optimization. Concepts such as business analytics, data science, and artificial intelligence are omnipresent in decision-makers’ mindset and ground to a large extent on machine learning. Familiarizing course participants with these concepts and enabling them to purposefully apply cutting-edge methods to real-world decision problems in management, policy development, and research is the overarching objective of the course.

This course has the goal of making students familiar with the problem of endogeneity and potential remedies. This implies that it will cover the opportunities and problems associated with traditional approaches (e.g., Instrumental Variable estimation, Matching, Difference-in-Difference) as well as more recent developments (e.g., Gaussian Copulas; Machine Learning and Causal Inference; Synthetic Control Methods; Directed Acyclical Graphs (DAG)). The course will also cover how the data structure (e.g., panel data) can be utilized to address the problem. Because the literature on endogeneity is often quite technical, this course aims at providing an easily accessible approach to this topic. Special emphasis will also be given to understanding when endogeneity indeed poses a real problem as compared to settings in which endogeneity is less likely to be a real threat to the validity of the findings.

Demand is an important quantity in many optimization problems such as revenue management and supply chain management. Demand usually depends on “supply” (price and availability of products, f. e.), which in turn is decided on in the optimization model. Hence, demand is endogenous to the optimization problem. Choice-based optimization (CBO) merges discrete choice models with math programs. Discrete choice models (DCM) have been applied by both practitioners and researchers for more than four decades in various fields. DCM describe the choice probabilities of individuals selecting an alternative from a set of available alternatives. CBO determines (i) the availability of the alternatives and/or (ii) the attributes of the alternatives, i.e., the decision variables determine the availability of alternatives and/or the shape of the attributes. We present CBO applications to location planning, supply chain management, assortment and revenue management.

This course has the goal of making students familiar with the problem of endogeneity and potential remedies. This implies that it will cover the opportunities and problems associated with traditional approaches (e.g., Instrumental Variable estimation, Matching, Difference-in-Difference) as well as more recent developments (e.g., Gaussian Copulas; Machine Learning and Causal Inference; Synthetic Control Methods; Directed Acyclical Graphs (DAG)). The course will also cover how the data structure (e.g., panel data) can be utilized to address the problem. Because the literature on endogeneity is often quite technical, this course aims at providing an easily accessible approach to this topic. Special emphasis will also be given to understanding when endogeneity indeed poses a real problem as compared to settings in which endogeneity is less likely to be a real threat to the validity of the findings.

The international EOSC Symposium will take place in Berlin in 2024. 450 experts from research and research infrastructures as well as representatives of European science policy are expected in Berlin to present concrete results, discuss new ideas and promote the future of Open Science in Europe. This outstanding event as well as the high thematic synergies have inspired the Open Science Conference to join forces with the EOSC Symposium to present the topic of Open Science in Berlin in 2024. Please save this date. Further information on the program and registration will be provided at a later date.

In dieser Veranstaltung lernen Sie die Merkmale offener Bildungsmaterialien kennen und reflektieren deren Chancen und Herausforderungen. Besonderes Augenmerk liegt auf dem Urheberrecht und der offenen Lizenzierung. In interaktiven Übungen erfahren Sie, wie Sie offen lizenzierte Bildungsmaterialien in Ihre Lehre einbinden können und was bei der Erstellung eigener OER zu beachten ist. Gemeinsam nehmen wir didaktische Fragen rund um OER in den Blick und diskutieren Qualitätsdimensionen offener Bildungsmaterialien

Eine wesentliche Zielsetzung des Kurses ist es, für die verschiedenen Strömungen der Wissenschaftstheorie zu sensibilisieren und grundlegende Kenntnisse über verschiedene wissenschaftstheoretische Begründungsstrategien zu vermitteln, um eine kritische Reflektion über die eigene wissenschaftliche Forschung zu ermöglichen. Obwohl der Schwerpunkt des Kurses auf der Diskussion philosophischer (wissenschaftstheoretischer) Positionen liegt, ist es ein weiteres Anliegen des Kurses, deren Bedeutung für die wissenschaftliche Praxis zu thematisieren und anhand praktischer Beispiele zu analysieren.