Research as a basis for professional archiving and open science
The research area of “research data management” focuses on the professional handling of data in an interdisciplinary manner. Specifically, it covers topics related to archiving practices – including data processing and provision within data infrastructures – as well as issues of data sharing and reuse in the context of open science and open data. In this field, it is essential to distinguish between the various types of research data used in the social sciences. For example, survey data and digital behavioral data present different challenges for data management, including preparation and documentation.
Legal aspects, research ethics, and data security are relevant across all subfields and for all data types. Challenges arise from, for example, the growing volume of individual-level data, some of which is sensitive, new data sources (such as from social media), new possibilities for linking datasets, and the question of how to offer such data securely yet in a user-friendly manner within increasingly interconnected research environments.
Given the large and growing collection of archived studies at GESIS, along with our services for data archiving and reuse, research in this area plays a key role in the expansion and development of related infrastructures. This field builds on insights from both applied and methodological social science research as well as from computational methods. It also provides numerous opportunities to explore issues of data quality – both at the level of individual datasets and entire data repositories, including concerns related to representativeness, trustworthiness, and usability.
Data Quality
Research data management processes are primarily focused on enhancing the quality of research data.
Through our work in this field, we contribute to a deeper understanding and further development of processes for the professional handling of research data. This includes, for example, quality assurance through long-term data availability, improvements in data documentation, and the advancement of secure and trustworthy data infrastructures.
The research area also addresses extrinsic data quality by emphasizing the (re)usability of existing data within the research community. Specifically, this involves the dimensions of the FAIR criteria (Findable, Accessible, Interoperable, Reusable).
Archiving practices
GESIS focuses on archiving practices, metadata standards, data linking, and trusted environments to enhance data quality, address legal and ethical challenges, and support interconnected research infrastructures in the social sciences.
Managing different types of data
Research focuses on managing diverse data types – especially digital behavioral data – by developing new approaches for archiving and ethics, while also advancing survey methods and exploring data linkages across sources.
Open Science, Reproducibility and FAIRness
This research examines data and code sharing in academia, focusing on motivations, barriers, and Open Science practices – aiming to enhance reproducibility, reusability, and alignment with FAIR principles.