Winners of the Open ODISSEI eScience Call 2022 announced

9 Feb 2023 - 4 min

Winners of the Open ODISSEI eScience Call 2022 announced - winning proposals

The Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI) and the Netherlands eScience Center are pleased to announce five winning proposals for the joint Open ODISSEI eScience Call 2022. These projects resulting from this joint call are funded by ODISSEI. This call supports researchers whose main area of expertise is in the Social Sciences.   
Each of the projects receives a grant consisting of three months of in-kind support by Research Software Engineers from the eScience Center.   

The winning projects are:   

The nature of EU rules: strict and detailed, or lacking bite? 

Dr. G. J. Brandsma, Radboud University

The European Union (EU) rules touch upon virtually all aspects of human and economic life, but little is known about their strictness. The goal of the project is to conduct a large-scale content analysis of all European directives and regulations ever adopted to determine if EU law is mostly strict and detailed or generally lacking bite. Furthermore, we will explore if this varies between issue areas, who is affected by EU law, and if it has changed over time, as little is known about EU law strictness, and currently, no research project has ever analyzed the contents of the full body of EU law.

Vocalisations of Disgust across Behavioural Contexts: A Computational Analysis  

Prof. dr. J. M. Tybur, VU Amsterdam 

The project aims to discover when and why humans systematically produce disgust vocalizations. Disgust vocalizations serve the crucial evolutionary function of motivating avoidance of pathogens and might also communicate moral standards or lack of interest. Together with research software engineers (RSEs) at the eScience Center, we will test whether there are acoustically different kinds of disgust vocalizations associated with different contextual features.

Political Polarisation and Residential Segregation  

Prof. dr. J. Tolsma, University of Groningen 

This project aims to study the relationship between residential segregation and political polarization in the Netherlands. To do this, we will using geographic data, election results and machine learning algorithms to predict where polarization is likely to occur. In addition, we will seek to identify geographical outliers with respect to segregation and polarisation to understand how these phenomena are related and where they may mutually reinforce each other.

From the Ivory Tower to Public or Private Power? The Role of Gender and Ethnicity in PhD-to-Labor-Market Linkages  

Dr. B. Hofstra, Radboud University 

This project explores gender and ethnic inequality in the labour market, focusing on the underrepresentation of women and ethnic minorities among professors in the academic labor market. We will use big data of nearly all Dutch PhDs from 1990-2021 to study how gender, ethnicity, and social networks influence labor market outcomes, both within and outside of academia, by cross-linking PhD data to non-science career databases to explain labour market differences of Dutch PhD-recipients.

One-click deployment for running simulations in the cloud mimicking an AI-aided screening process  

Prof. dr. R. van de Schoot, Utrecht University 

The proposal seeks to develop a one-click-deployment option for running large-scale simulation studies in the cloud using open-source software ASReview. Using this software, we aim to screen large amounts of textual data, test the existing one-click deployment functionality and investigate the run-time differences between locally running a simulation study. We do this by using the deployment options and add the one-click deployment option to the simulation pipeline in order to quickly run a large-scale simulation study for the Netherlands Environmental Assessment Agency (PBL) and similar institutes. By providing this study to these institutes, we can select the best-performing model for answering research questions from the Dutch government.

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