Data
We aim to provide complete data sets and analysis workflows for our studies.
Curations
Literature Reviews
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The Literature Reviews in Information Systems curation (GitHub Repository) compiles and maintains an evolving dataset of literature reviews in Information Systems, supported by a dedicated bibliographic database and automated update routines. It provides a foundation for literature reviews and meta-research on review practices in IS.
Highlights
- Curated, extensible dataset (
records.bib) - Reproducible update workflow (
make update) - Focus on evidence synthesis methods and trends in IS research
GenAI Prompts for Reviews
The GenAI Prompts for Literature Reviews curation provides a curated collection of prompts designed to support standalone literature review papers using generative AI tools such as ChatGPT. The repository structures prompts along the core stages of the literature review process and is grounded in peer-reviewed methodological research on GenAI-supported knowledge synthesis.
Coverage of the Review Process
- Problem formulation
- Search
- Screening
- Quality assessment
- Data extraction
- Data analysis
Highlights
- Academically evaluated and peer-reviewed prompt designs
- Full coverage of the literature review workflow
- Reusable and adaptable prompts for research, teaching, and supervision
- Open for continuous updates and community contributions
This resource supports methodologically rigorous use of GenAI while explicitly avoiding full automation or domain-specific reviews, emphasizing transparency, reflexivity, and researcher control.
Organizational Handbooks
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The Organizational Handbooks curation collects public employee and academic handbooks from digital-by-default organizations and research labs. It enables comparative analyses of digital work practices, governance models, and organizational transparency.
Highlights
- Structured inventory of repositories and open handbook resources
- Includes organizational, community, and academic research handbooks
- Basis for qualitative and computational content analysis