We collaborate with various researchers from fields like Historical Network Research, mathematical modeling and Social Network Analysis.
Alberto Baccini (University of Siena)
based in the science of science field, develops and applies multilayer network approaches to investigate the complex linkages between social communities as producers of science, scientific artifacts (journals, articles) and concepts. He studies changes over time in the discipline economics and is interested in the applicability of multilayer network analysis to different types of (historical) network data.
Aline Deicke (University of Marburg, ADW Mainz)
uses network analysis to investigate the collaborative processes that lead to the generation of knowledge, ideas and literary works in the social circles of Early Romanticism. Aiming at complementing epistolary data with data on social relations, she is interested in the social structure of the time, as well as the identification of unacknowledged contributions, especially by historically marginalized groups.
Malte Doehne (Universtiy of Zurich)
is a sociologist who uses mostly quantitative methods, notably network analysis, to study innovation diffusion and societal change. Among other topics, he investigates relational antecedents to the adoption of scientific innovations and is working on a multi-layered, ecological approach to studying network dynamics at different time-scales. In the context of the ModelSEN-project, he is particularly interested in developing best practices for evaluating the quality of relational data and dealing with missing data.
Marten Düring (University of Luxembourg)
based in the field of digital history, works on various projects positioned on the intersection between historical thinking, novel computational methods and software design. Together with the ModelSEN-project, he aims at to investigate possible scenarios for ontology usage within the Historical Network Research (HNR) community and to ultimately develop a shared ontology.
Heiner Fangerau & Thorsten Halling (University of Duesseldorf)
are interested in the evolution of knowledge as a networking process. They investigate scientific communication and connections and share with the ModelSEN-project the goal of developing normed historical (network) data in the broader scope of making science related network data reliable and accessible.
Catherine Herfeld (University of Zurich)
works in the field of history and philosophy of the social sciences. Using network analysis, she investigates the influence of different network positions in the diffusion process of scientific innovations in the field of economic history and furthermore studies model transfer in economics analyzing scientometric data. In the context of the ModelSEN-project, she considers the discussion of best practices in the history of science for dealing with issues of data storage, access, and robustness to be particularly important.
Charles van den Heuvel (University of Amsterdam, Huygens Institute)
is interested in the process of knowledge transfer in intellectual and technological networks and investigates scientific communication by different media, such as letters and drawings. With the ModelSEN-project, he shares the goal of developing shared ontologies for historical (network) data in the broader scope of making epistolary data accessible as linked data.
Eero Hyvönen & Petri Leskinen (Aalto University)
work in the field of computer science and digital humanities with a focus on semantic web technologies. Their current project LetterSampo aims at the linkage of historical letter collections through providing a standardized data model for epistolary data. With the ModelSEN-project, they share the interest in ontology building for historical network analysis, and a collaborative approach to their research project.
Roberto Lalli (DIMEAS, Politecnico di Torino)
is a historian of science with expertise in the history of Physics. In his research he focuses on the social, political, and epistemic aspects of knowledge production, from the second half of the nineteenth century to the present. A paralell line of research concerns the intertwining of science and politics in the growing field of Science-Diplomacy.
Dirk van Miert (Utrecht University, Huygens Institute)
based in the fields of early modern cultural history and history of knowledge, studies the resilience of the transnational, intellectual community of the Republic of Letters through phases of societal change and epistemic revolutions. With the ModelSEN-project, he shares the aim of the broad use of linked data and authority files for metadata in the context of historical network analysis.
Eugenio Petrovich (University of Siena)
studies the social structure of the field of analytical philosophy by extracting acknowledgement data from scientific publications, identifying communities of scholars, and investigating the interplay of social structure and intellectual organization. As part of the ModelSEN-project, he is interested in discussing the concepts used to interpret methods within the (historical) network research community.
Paolo Rossini (Erasmus University Rotterdam)
looks at the role of tie strength and homophily in the formation and evolution of historical social networks, investigating the social conditions that allow scientific innovations to spread and find acceptance. In the context of the ModelSEN-project, he would like to take the opportunity to complement different datasets and discuss the issues of data storage, access, and robustness.
Simone Turchetti & Carrington Kinyanjui (University of Manchester)
explore the role of scientific data exchange in the formation of the current scientific system and how biases in these structures are setting the stage for current unbalances.
Ingeborg van Vugt (Utrecht University)
works on structural balance in signed networks of early modern correspondence, combining network analysis with close reading of letters. As part of the ModelSEN-project, she would like to exchange best practices for data merging und disambiguation, and think about further possibilities of the analysis of negative ties in social networks (gradation, use of sentiment analysis).