Event Calendar

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.

Online-Workshops zum wissenschaftlichen Publizieren (nicht nur) für Promovierende. Im Rahmen dieser ersten Veranstaltung sollen u.a. folgende Themenfelder ausgemessen werden: Rechtsfragen – Publikationsstrategie – Verlagsvertrag – Publikationskosten – Wissenschaftsmarketing.

Online-Workshops zum wissenschaftlichen Publizieren (nicht nur) für Promovierende. Im Rahmen dieser zweiten Veranstaltung sollen u.a. folgende Themenfelder ausgemessen werden: Wissenschafts- und förderpolitische Rahmenbedingen – Rechtsfragen – Erst- und Zweitveröffentlichung im Open Access – Publikationskosten – Open Access-Infrastrukturen in der Region.

The conference will offer an opportunity for members to come together with OASPA to share ideas, knowledge and ask questions.

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.

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

Online-Workshops zum wissenschaftlichen Publizieren (nicht nur) für Promovierende. Im Rahmen dieser dritten Veranstaltung sollen u.a. folgende Themenfelder ausgemessen werden: – Digitale Bildreproduktion und Wissenschaft – Schutzrechte am Bild – Grenzen des Urheberrechtsschutzes – Nutzungsvereinbarungen – Open GLAM.

Was ist Open Science? Auf welche Weise können in der Wissenschaft Offenheit und die Kultur des Teilens praktiziert und gefördert werden? Welche Methoden gibt es, um mehr Offenheit in der wissenschaftlichen Praxis zu leben? Unter dem Motto Meet, Share, Inspire, Care bietet das 3. Open Science Festival in Deutschland die Gelegenheit, diesen und anderen Fragen nachzugehen, sich zu vernetzen und voneinander zu lernen.

Online-Workshops zum wissenschaftlichen Publizieren (nicht nur) für Promovierende. Im Rahmen dieser vierten Veranstaltung sollen u.a. folgende Themenfelder ausgemessen werden: – Wissenschafts- und förderpolitische Rahmenbedingen – Urheber- und Datenschutzrecht – Archivierung – Publikation – Recherche.

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.

Acknowledging the profound influence of recent advancements in AI and big data analytics on research, the conference also highlights how methodological futures needs to be in continuity with the past and embedded in community, highlighting the invaluable role of indigenous knowledge, participatory research, and interdisciplinary approaches in developing sustainable solutions. These varied methods and methodologies offer a holistic view of environmental complexities and pave the way for future innovative strategies geared toward resilience and sustainability.

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 year our Open Science Day will feature Best Practices in Open Science from researchers at the University of Mannheim including Open Science implementations in Economic History, Software Engineering and the value of connecting research with society. Additionally, we will also feature lightning talks from our newest Open Science Grant 2024 awardees and national efforts to improve Open Science and reproducibility from members of ther German Reproducibility Network.

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.

Do you know that feeling, when you work on your research project after not having looked at it for a while (for example after receiving reviews) and you can’t remember how the different parts fit together? And when you finally figure it out again, the results are somehow different. Oh no! In this course we will set you up to never run into these kinds of issues again. Let’s get your research project organized, under version control, stable, and published so that you can confidently say that your research is reproducible.
25.09. | 02.10. | 09.10. | 16.10. | 23.10.

Leibniz Open Science! oder kurz LOS! ist eine Online-Reihe mit dem Ziel, die praktische Anwendung von Open Science zu fördern und über aktuelle Entwicklungen rund um Open Science innerhalb und außerhalb der Leibniz-Gemeinschaft zu informieren.

Mit dem einmal jährlich stattfindenden OERinfo-Fachtag möchten wir ein Format etablieren, das den Austausch und die Vernetzung der wachsenden OER-Community unterstützt. Dieses Forum soll Wissenschaft wie Praxis, etablierten wie neu anlaufenden OER-Initiativen (auch außerhalb bekannter Förderrichtlinien), OER-Befürworter*innen und OER-Interessierten gleichermaßen dienen.

Im Rahmen der diesjährigen Konferenz der Plattform Privatheit wollen wir uns interdisziplinär mit den Gestaltungsherausforderungen und -möglichkeiten auseinandersetzten, um Freiheit und Selbstbestimmung in digitalen Infrastrukturen zu stärken und unerwünschte Entwicklungen zu vermeiden. Diskutiert werden sollen technische, ökonomische, soziale, politische, rechtliche, kulturelle und pädagogische Ansätze, um den Schutz der Privatsphäre und der informationellen Selbstbestimmung in der digitalen Welt weiterzuentwickeln. Dabei werden normative, institutionelle und instrumentelle Konzepte eines freiheitsfördernden Datenumgangs im Kontext digitaler Infrastrukturen diskutiert. Es sollen auch konstruktive Bausteine für eine zukunftsgerechte Gewährleistung individueller und kollektiver Selbstbestimmung und Grundrechte erörtert werden.

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.

We are preparing a thought-provoking programme for you! It is divided into twenty-six sessions that address, among other things, the ambitions of the EOSC Tripartite Partnership, the EOSC Federation, the launch of the EOSC EU Node, the EOSC landscape, including the European Common Data Spaces and the Artificial Intelligence trends, and the impact and contributions of stakeholders.