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Topic · Sociology and Political Science

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. 10.4% 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%
514
per year above
Top-10% rate
10.4%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+11%
the tick is no change
Largest producer
Germany
27% of the 10 largest, 2022-now
Data Analysis and Archiving

Which countries lead Data Analysis and Archiving research?

By volume, Germany and the United Kingdom publish the most (284 and 236 works in 2022-now).

By volume, 2022-now

  1. 1 Germany 284 works
  2. 2 United Kingdom 236 works
  3. 3 United States 220 works
  4. 4 France 81 works
  5. 5 Canada 56 works
  6. 6 Australia 49 works
  7. 7 Netherlands 42 works
  8. 8 Switzerland 31 works
  9. 9 Hungary 26 works
  10. 10 Austria 23 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.

Germany: 27.1%United Kingdom: 22.5%United States: 21.0%France: 7.7%6 others listed: 21.7%27%largest
Germany284 · 27.1%United Kingdom236 · 22.5%United States220 · 21.0%France81 · 7.7%6 others listed228 · 21.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Data Analysis and Archiving research?

By volume in 2022-now, Leibniz Association publishes the most Data Analysis and Archiving research, followed by Centre National de la Recherche Scientifique and University of Edinburgh.

By volume, 2022-now

  1. 1 Leibniz AssociationGermany 27 works
  2. 2 Centre National de la Recherche ScientifiqueFrance 26 works
  3. 3 University of EdinburghUnited Kingdom 13 works
  4. 4 University College LondonUnited Kingdom 12 works
  5. 5 Helmholtz Association of German Research CentresGermany 11 works
  6. 6 University of BremenGermany 9 works
  7. 7 Humboldt-Universität zu BerlinGermany 8 works
  8. 8 University of OxfordUnited Kingdom 8 works
  9. 9 Ludwig-Maximilians-Universität MünchenGermany 6 works
  10. 10 University of ManchesterUnited Kingdom 6 works

Who are the leading researchers in Data Analysis and Archiving?

The most-cited researchers publishing on Data Analysis and Archiving include David Moher, Virginia Braun and Victoria Clarke.

  1. 1 David Moher Canada 898k citations
  2. 2 Virginia Braun New Zealand 286k citations
  3. 3 Victoria Clarke United Kingdom 285k citations
  4. 4 Anselm L. Strauss United States 205k citations
  5. 5 Paul M. Thompson United States 196k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Data Analysis and Archiving?

The most cited paper in Data Analysis and Archiving is "Whatever happened to qualitative description?" (Margarete Sandelowski, 2000), with 12,287 citations. Next come "What's in a name? Qualitative description revisited" (5,038) and "To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales" (4,766).

Most cited papers
#PaperReleasedCitations
1 Whatever happened to qualitative description? Margarete Sandelowski · Research in Nursing & Health Open access 2000 12,287
2 What's in a name? Qualitative description revisited Margarete Sandelowski · Research in Nursing & Health 10 Dec 2009 5,038
3 To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales Virginia Braun, Victoria Clarke · Qualitative Research in Sport Exercise and Health Open access 26 Dec 2019 4,766
4 Qualitative Data Analysis for Health Services Research: Developing Taxonomy, Themes, and Theory Elizabeth H. Bradley, Leslie Ann Curry, Kelly J. Devers · Health Services Research Open access 24 Jan 2007 3,529
5 Ethics, Reflexivity, and “Ethically Important Moments” in Research Marilys N. Guillemin, Lynn H. Gillam · Qualitative Inquiry Apr 2004 2,641
All 99 most cited papers, with filters and sorting

Where is Data Analysis and Archiving research done?

The largest centres of Data Analysis and Archiving research in 2022-now are London (United Kingdom), Berlin (Germany), Paris (France) and Budapest (Hungary). Among places with at least 20 works in it, it is an unusually large share of all research in Berlin.

Largest cities, 2022-now

  1. 1 LondonUnited Kingdom 50 works
  2. 2 BerlinGermany 34 works
  3. 3 ParisFrance 25 works
  4. 4 BudapestHungary 18 works
  5. 5 MunichGermany 16 works
  6. 6 EdinburghUnited Kingdom 15 works
  7. 7 MelbourneAustralia 15 works
  8. 8 New YorkUnited States 13 works
  9. 9 ViennaAustria 13 works
  10. 10 Frankfurt am MainGermany 12 works

Where it is the local speciality

  1. BerlinDE · 33.8 works8.9×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Data Analysis and Archiving than the world average.

See Data Analysis and Archiving on the map

Where is the best place to study Data Analysis and Archiving?

Among universities, judged by research, University College London, University of Edinburgh and University of Bremen 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.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1University College LondonUnited Kingdom 84.020.2%6.6×12 +17.1%
2University of EdinburghUnited Kingdom 71.66.9%12.8×13 +21.1%
3University of BremenGermany 20.01.9%33.5×9 +134.2%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Data Analysis and Archiving, country by country

Is Data Analysis and Archiving research growing?

Output in 2022-2025 was 11% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Data Analysis and Archiving.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Data Analysis and Archiving as a percentage of every work published that year, 0.0049% in 2024.

still counting0.0025%0.0050%0.0075%Data Analysis and Archiving0.0067% in 202519801990200020102020

Fractional works of Data Analysis and Archiving over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Data Analysis and Archiving10.4% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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