Data Analysis and Archiving
Data Analysis and Archiving is a research topic within Sociology and Political Science. Science Explorer counts 4.9k research works in it since 1952. 28.1% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the ethical and methodological considerations of conducting secondary analysis of qualitative data, including issues related to data sharing, archiving, and the contextualization of data. It also explores the challenges and opportunities in reusing qualitative data for social research and emphasizes the importance of addressing epistemological concerns in such analyses.
- Qualitative Data
- Secondary Analysis
- Ethical Issues
- Data Sharing
- Archiving
- Research Methodology
- Social Research
- Epistemological Challenges
- Contextualizing Data
- Longitudinal Studies
- Research works
- 4.9k fractional, since 1952
- In the world top 10%
- 1.4k per year above
- Top-10% rate
- 28.1% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +29% the tick is no change
Which countries lead Data Analysis and Archiving research?
By volume, the United Kingdom and Germany publish the most (200 and 194 works in 2022–2025).
By volume, 2022–2025
- 1 United Kingdom 200 works
- 2 Germany 194 works
- 3 United States 180 works
- 4 France 68 works
- 5 Canada 46 works
- 6 Australia 45 works
- 7 Netherlands 28 works
- 8 Switzerland 22 works
- 9 Hungary 20 works
- 10 Austria 20 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Data Analysis and Archiving research?
By volume in 2022–2025, University of Edinburgh publishes the most Data Analysis and Archiving research, followed by University College London and University of Bremen.
By volume, 2022–2025
- 1 University of EdinburghUnited Kingdom 12 works
- 2 University College LondonUnited Kingdom 9 works
- 3 University of BremenGermany 7 works
- 4 University of OxfordUnited Kingdom 6 works
- 5 University of GlasgowUnited Kingdom 6 works
- 6 University of SouthamptonUnited Kingdom 5 works
- 7 Humboldt-Universität zu BerlinGermany 5 works
- 8 University of ManchesterUnited Kingdom 5 works
- 9 Eötvös Loránd UniversityHungary 5 works
- 10 GESIS - Leibniz Institute for the Social SciencesGermany 5 works
Where is Data Analysis and Archiving research done?
The largest centres of Data Analysis and Archiving research in 2022–2025 are London (United Kingdom), Paris (France), Berlin (Germany) and Melbourne (Australia). Among places with at least 20 works in it, it is an unusually large share of all research in Berlin.
Largest cities, 2022–2025
Where it is the local speciality
- BerlinDE · 20.0 works6.7×
Location quotient: how much more of its research is in Data Analysis and Archiving than the world average.
Where is the best place to study Data Analysis and Archiving?
Among universities, judged by research, University of Edinburgh and University College London score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | University of EdinburghUnited Kingdom | 60.0 | 23.3% | 15.2× | 12 | +164.2% |
| 2 | University College LondonUnited Kingdom | 40.0 | 38.1% | 6.5× | 9 | +117.1% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Data Analysis and Archiving research growing?
Output in 2018–2022 was 29% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Data Analysis and Archiving.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.