Sub-Project INF: Further Development of the Global Welfare State Information System (WeSIS), eScience Services and Research Data Management

• 2022 – 2025
• Prof. Dr. Andreas Breiter

Project management: Prof. Dr. Andreas Breiter

Funding institution: DFG – German Research Foundation

The information infrastructure project (INF project) “Advancing the Global Welfare State Information System (WeSIS), eScience Services and Research Data Management” has four main objectives: (1) to further develop the Global Welfare State Information System and open WeSIS to the scientific community; (2) to provide eScience services and coordinate the research data management of CRC 1342; (3) to extend WeSIS by providing new data on social policy positions of political parties and new data on spatial development disparities; and (4) to develop innovative methods in the field of computational social sciences and information visualization together with the projects.

1. Building on the preliminary work in the first funding period (sub-project A01), the first goal is to systematically expand WeSIS as a central, web-based information system for social science data with a focus on “global social policy”. Five steps are necessary to achieve this goal: (a) the coordination of the SFB 1342 cross-project data collection on scope of services and inclusivity and the harmonization of the newly collected data; (b) the expansion of WeSIS to include the storage of text data in order to integrate the data collected in project area B; (c) the implementation of tools to support the sub-projects in the analysis and visual presentation of their research data; (d) the adaptation of the IT infrastructure for future use cases; (e) the opening of WeSIS to the general public, supported by the publication and dissemination of innovative information materials, multimedia support at international conferences and the provision of training courses.
2. The INF project serves as a service project for the research data management of the SFB 1342 along the entire research data cycle in compliance with the FAIR principles. It offers regular training courses in cooperation with the University of Bremen and the State and University Library (SuUB) as well as external (GESIS) and internal partners at SOCIUM (QualiService and Data Center of the FGZ; BIGSSS). In addition, an eScience environment is offered to support and strengthen internal communication and collaboration. All services are embedded in the local infrastructure of the University of Bremen (Data Science Center, IT Service Center).
3. The INF subproject will conduct a joint data collection with the subprojects A06 and B04 on political parties and their ideological orientation based on a new corpus of party documents. It extends the basis of WeSIS by adding data on socio-economic inequalities within and between countries to identify patterns of spatial inequalities as a result of social policy. It thus provides additional data that can be used as (un)dependent variables in the analyses of the B-area projects.
4. The quality and quantity of data has improved considerably during the first phase of SFB 1342, making AI methods a realistic option. Therefore, the INF subproject will work on innovative procedures based on AI methods, which will first be tested in pilot studies and then made available to all projects. The focus will be on methods such as argumentation mining, text summarization and new forms of multimodal information visualization.