aims to open up new avenues for the social sciences
Building on the strengths of existing panel studies, innovating new methods and procedures, and complementing them with the intelligent integration of data derived through new technologies.
Our society faces a number of major social challenges in the coming decades. To expand and enrich our understanding of these challenges and the processes that underlie them, the social sciences need to go beyond the current data sources and methods and unlock emerging opportunities in new data spaces. Social science research needs to exploit the potential of new forms of data from administrative processes, data analytics and machine learning, digital communications and mobility, and other sources. It also needs new forms of data acquisition (such as virtual interviews, bots and augmented reality) alongside the ongoing development and expansion of well-established longitudinal survey programmes. These new data forms and data linkages must be initiated, developed and subjected to rigorous quality testing.
The Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) founded the Infrastructure Priority Programme “New Data Spaces for the Social Sciences” (SPP 2431) in December 2022 as a long-term funding scheme.
Whereas a regular DFG-Priority programme (SPP) is for up to 6 years (consisting of two 3-year phases) the New Data Spaces programme may be funded for longer than six years, depending on the success of the overall programme.
1. Develop best-practice examples of data generation, data collection and data integration to enhance data quality.
2. Anchor innovations in existing panel studies, new data collection programmes and data analytic approaches.
For each funding phase the programme will invite project proposals in and across four main research areas that together aim to achieve the programme objectives.
In contemporary social science research, the integration of survey data with other data sources is becoming increasingly vital. Linking survey data with administrative records, geographic information, multimodal behavioral data, Big Data, or synthetic data can enhance analytical potential, reduce respondent burden, and improve data quality and cost-efficiency. These linkages allow researchers to fill information gaps, validate self-reports, and address methodological challenges like attrition and bias. However, different linkage strategies raise distinct technical, ethical, and legal considerations around data protection and access, necessitating innovative research to develop viable methodological frameworks.
Survey research faces fundamental challenges in maintaining representative samples as participation rates continue to decline across all data collection modes, including difficulties reaching potential participants, growing unwillingness to participate in scientific surveys, and particular obstacles in recruiting marginalized and hard-to-reach populations. In response, researchers have developed innovative methodological approaches including adaptive recruitment strategies, specialized sampling techniques for hidden populations, and responsive data collection designs. While these approaches are promising, significant knowledge gaps remain and research is needed to develop evidence-based guidelines for their optimal implementation and effectiveness in different research contexts.
Technological advancements have created new possibilities for data collection, but many questions about method-inherent biases and the generalizability of data from new collection modes remain. At the same time, rapidly changing institutional and social environments can also demand the measurement of entirely new constructs. The field must address three interconnected validity challenges: systematic biases inherent in different research methods, especially those using new technologies, difficulties in generalizing findings as non-probability sampling methods become more common, and the need to adapt existing instruments for modern data collection environments or potentially develop new instruments for existing and new constructs. Innovative research that successfully addresses these challenges will require interdisciplinary collaboration and methodological innovation that integrates traditional survey approaches with AI and computational methods.
Multimodal data acquisition represents a transformative frontier in panel study research, expanding beyond traditional survey methods and data spaces to capture rich, contextual information through diverse technological approaches. This research area encompasses online surveys and assessments, app-based data collection, ecological momentary assessments, and virtual and augmented reality interfaces. It also covers automated interview systems that leverage real-time behavioural, physiological and environmental data, as well as the specific employment of generative AI tools and systems. These emerging methodologies offer unprecedented opportunities to understand human behaviour and social processes as they unfold naturally across different contexts and time scales. However, innovative research is needed to increase our understanding of the methodological, technical, and ethical questions that need to be addressed to realize the full potential of these methods in the context of panel studies.
The first cohort of fifteen New Data Spaces projects was funded in 2024. All New Data Spaces projects are supported and accompanied by a set of unique measures developed and provided by a newly established Research Infrastructure and Innovation Lab (ENTAILab). In addition to providing infrastructural services, consultation, and supports to projects, ENTAILab also offers outstanding opportunities for research on data collection, processing and analytics, and using existing panel studies.
Read more about ENTAILab.
hosts New Data Spaces for the Social Sciences' Coordination and Management Project (CONNECT) and ENTAILab Measure 4: Results for Future Data Spaces and Open Science
co-hosts ENTAILab Measure 1: Building on and Developing Existing Panel Studies
hosts ENTAILab Measure 3: Data Protection and Data Ethics.
hosts ENTAILab Measure 2: Research-driven Infrastructure for Advanced Survey-related Data (CIRCLET)