Key indicators summarise the report’s central metrics. All values are calculated from the underlying dataset and refer to the full period unless otherwise stated. Percentages (peer-reviewed, Open Access, international collaboration) are calculated as a share of total publications per year.
Percentage change is not shown when the base value is below 10 units, as small base values produce statistically unstable percentages (Hicks et al. (2015), principle 8; cf. CDC rule for n < 16). Absolute values are shown instead.
Insights
During the period 1970-2025, the
compound annual growth rate (CAGR) was +8.2%. The trend is
increasing (statistically significant; p < 0.05).
Mann
(1945);
Sen
(1968) The following years had unusually high publication activity:
2019, 2021, 2022, 2023, 2024.
Insights
The peer-reviewed share: 100 % recent
decade (2017–2026), up from 100 % previous decade (2007–2016). Long-term
trend (1970–2026): increasing.
3276 publications (100%) scientific, 0 publications (0%) other.
Older years (1970–2018) aggregated for readability. Full timespan available in data export.
The Norwegian Publication Indicator (NPI), also known as the Norwegian list, classifies publication channels into two levels. Level 2 (top ~20% per field) is considered the most prestigious channels. Level 1 covers other approved channels.
A high proportion of publications (≥10%) lack NPI classification. Common causes: missing ISSN in source data, channels outside the register (~40,000 journals), conference series, or recently launched journals. See the journal table for unclassified channels.
DORA mode is enabled for this report. Bifrost evaluates the report against the principles of DORA (2012) and CoARA (2022).
The report contains elements that are not compatible with DORA principle 1: “Do not use journal-based metrics as a surrogate measure of the quality of individual research articles.”
NPI level classification (Level 1/2) is shown in the report. NPI is a Nordic classification system that ranks publication channels by academic standing, i.e. a channel-based ranking that DORA advises against using as a quality surrogate. The classification is presented here as descriptive information about publication patterns, not as a measure of individual article quality.
Researchers are listed below, sorted by scientific productivity.
Insights
The top 20% most productive researchers
account for 40% of publications (Gini 0.22, scale: 0 = even, 1 = fully
concentrated). Average number of co-authors: 2.5 recent decade
(2017–2026), up from 2.1 (2007–2016).
Insights
171 research groups of roughly equal
size; no single cluster dominates. Each researcher collaborates with an
average of 3.1 others (a moderately connected network). Clear cluster
structure (Modularity 0.99); researchers primarily work within their own
group. The network is sparse: only 0.2% of all researcher pairs have a
direct collaboration link.
Each node represents a researcher and each link a co-authorship. Colors indicate research groups (clusters) identified via modularity analysis. Node size reflects number of publications.
The co-authorship network is built from co-authored publications. Each node represents a researcher, and each edge is weighted by number of joint publications. Edge weights are normalized using association strength Van Eck et al. (2009) before clustering with the Louvain algorithm. Centrality measures: degree (number of collaborators), collaboration intensity (total co-authoring frequency), and bridge score (weighted betweenness using inverse weights) Newman (2004). Network density measures the proportion of realized vs. possible collaborations. Terminology: «Collaborators (avg)» = mean degree; «Clustering» = modularity Blondel et al. (2008).
Percentages are calculated on pairs where both authors have country data (3207 classified of 3222 total, coverage 100%). Of which 15 pairs where both authors lack institutional affiliation, 0 pairs where the institution could not be mapped to a country, and 0 pairs where one side lacks data.
‘Co-authored texts’ indicates the number of texts the author has written together with one or more co-authors.
Network statistics show central nodes in the collaboration network. Degree is the number of direct collaborations, while betweenness shows which authors act as bridges between different groups.
The first co-author listed is the one the author has written with the most times. ‘Number’ indicates the number of co-authored texts with the author. Up to four additional co-authors are listed, in descending order of co-authorship.
Below is a visualization of the 171 different groupings in the dataset. The colors indicate different groups.
The co-authorship network comprises 822 researchers and 1081 collaborations. Due to the size of the dataset, a simplified version highlighting the strongest collaboration patterns (backbone analysis) is shown. Individual connections with few joint publications have been omitted for clarity.
Author names to the right; group ID to the left. You can see the size of the groups and the most common keywords of the groups in the tables that follow. A combination of search and sorting can be used to further explore group membership.
The table is limited to a) groups with more than 3 members; b) groups with at least one keyword in any publication; c) the ten most used keywords per group.
See also the supervisor and opponent network in the researcher section for an analysis of academic collaboration patterns beyond co-authorship.
Insights
336 institutions contribute. University
of Gothenburg, Umeå University and Stockholm University account for 25 %
— a broad distribution.
Overview of international collaboration based on co-authorship and affiliations in publications.
Insights
42 countries represented in
collaborations. Sverige, Norge and Finland are most common. 9.1 % of
publications involve international co-authors — up from 6 % (2007–2016)
to 11 % (2017–2026).
Based on co-author affiliation country.
Insights:
The network comprises 106 institutions
with 308 collaboration relationships. The strongest collaboration is
between Göteborgs universitet and Högskolan i Borås (29
co-publications). Göteborgs universitet has the most collaboration
partners (33).
Insights
Social Sciences dominates (80 %).
Subject breadth is stable (1970–2026, H: 0.71 → 0.72). Moderate
interdisciplinarity — research combines related subject areas.
Rao-Stirling: 0.526 (where 0 = single discipline, 1 = maximum
diversity). Based on 6 HSV main areas (Swedish classification).
Rao-Stirling
(Stirling, 2007)
Shannon H (evenness index) measures how evenly publications are distributed across subject areas. A value of 1.00 means perfect evenness; lower values indicate dominance by individual areas. Rao-Stirling measures interdisciplinarity by weighing both the distribution and the taxonomic distance between subject areas according to the Swedish classification system. The scale ranges from 0 (all publications in one subject) to 1 (maximum spread across distant subject areas).
Proportion of total publications per year (%). Note that a publication may belong to multiple categories.
There are 31 level 2 categories in the dataset. Showing the 25 most frequent here.
During 1970–2026, 5.1% (168 of 3,276) publications lack subject classification at this level.
Proportion of total publications per year (%). Note that a publication may belong to multiple categories.
genus; mathematics; learning; barn; idrottsvetenskap; slaviska språk; socialpsykologi; social psychology; socialvetenskap; medicin; biblioteks- och informationsvetenskap; estetiska ämnen; etnicitet; nordiska språk; kulturarv och kulturproduktion; medieteknik; lärande; idrott; kommunikation mellan människor; hälso- och sjukvård i samhället; teknikvetenskap; tillämpad matematik; morfologi; mathematics ; vårdvetenskap; genetics; språkvetenskap; pedagogical work; dentistry; ekonomi; artificial intelligence (ai)
Insights
Broad keyword profile — no single term
dominates (HHI: 0.0018 — Herfindahl-Hirschman Index, where 0 = perfectly
even distribution, 1 = one term dominates entirely). Most common is
“sweden” appearing in 6.2 % of publications, across a total of 6600.
education; education and learning; higher education; högskolan; högskolan sverige lärarutbildning; lärarutbildning; lärarutbildning och pedagogisk yrkesverksamhet; lärarutbildning– sverige; pedagogical work; pedagogics and educational sciences; pedagogik och utbildningsvetenskap; pedagogiskt arbete; sverige; teacher education; teacher education and education work; utbildning och lärande
Colors indicate frequency quantiles within this dataset.
Red: Highest frequency (6.2-4.98%); Blue: High frequency (4.98-3.76%); Green: Medium frequency (3.76-2.54%); Orange: Low frequency (2.54-1.32%); Gray: Lowest frequency (1.32-0.1%)
Declining keywords: science education, gender
No rising trends were identified. Below are deep-dive insights for declining keywords instead. These may indicate subject areas decreasing in relevance or research interest.
The following keywords had periods of high activity in the past but have since declined. The analysis shows when they peaked, what drove the interest, and how activity has evolved since.
Burst period: 2014–2018 (moderate burst)
Peak year: 2017 (9 pubs.)
Driving actors during period:
Co-varying keywords: naturvetenskapsämnenas didaktik, curriculum studies, gender
Current status: Declining
Burst period: 2021–2024 (moderate burst)
Peak year: 2022 (5 pubs.)
Driving actors during period:
Co-varying keywords: sweden, preschool, assessment
Current status: Stable
Burst period: 2016–2021 (moderate burst)
Peak year: 2018 (10 pubs.)
Driving actors during period:
Co-varying keywords: innovativt lärande, teacher education, sweden
Current status: Potentially declining
Burst period: 2016–2020 (moderate burst)
Peak year: 2018 (10 pubs.)
Driving actors during period:
Co-varying keywords: innovative learning, teacher education, sweden
Current status: Potentially declining
Burst period: 2015–2020 (moderate burst)
Peak year: 2016 (6 pubs.)
Driving actors during period:
Co-varying keywords: pedagogik med inriktning mot utbildningsvetenskap, lärarutbildning, teacher education
Current status: Stable
The heatmap shows how often keywords co-occur in the same publications. Association strength Van Eck et al. (2009) normalizes co-occurrence by the product of the individual keyword frequencies. Red asterisks (✱) in the upper-right corner of cells mark statistically significant co-occurrences (p < 0.05).
Keywords that frequently co-occur in the same publications form thematic clusters. The table below summarizes the clusters; the interactive graph shows the relationships visually.
The network diagram shows how keywords relate to each other based on co-occurrence in publications. Larger nodes mean more frequent keywords. Lines show co-occurrence. Colors indicate thematic clusters identified using the Leiden algorithm (Traag et al., 2019).
The frequency of individual words in the dataset as a whole. Words have been taken from title, abstract, and keywords. “Frequency” is total uses, including the number of mentions in the same text, while “publications” is the number of unique texts where the word appears.
Insights
The 5 most common words (by share of
publications) are: “education” (97%), “teacher” (96%), “swedish” (65%),
“teachers” (65%), “sweden” (56%). These patterns reflect the thematic
core of the dataset. Note that geographic markers such as “swedish” are
common in academic metadata and reflect the national affiliation of
publications rather than the research topic.
Notice: The dataset contains 32933 rows. For best performance, only the 8000 with the highest frequency are displayed in the table.
Here is a summary of word stems, i.e. parts of words, in the dataset as a whole. Words have been taken from title, abstract, and keywords. “Frequency” is total uses, including the number of mentions in the same text, while “publications” is the number of unique texts where the word appears.
These trends are indicative and complement the keyword analysis above.
Methodological note: Word frequency analysis is based on individual words extracted from title, abstract, and keywords. Unlike author-selected keywords, individual words can be noisier and more ambiguous — for example, the word ‘system’ may appear in both technical and social science contexts, while the keyword ‘adaptive systems’ is more precise. Stricter thresholds are used (minimum 10 occurrences, correlation > 0.5) and academic stopwords are excluded.
Rising/declining shows trends over time (Spearman correlation). New/disappearing shows lifecycle — when words started or stopped being used.
No statistically significant trends were identified among the most common words.
The OA analysis is based on 1581 publications with DOI matched against OpenAlex (48% of 3276 total). 1663 publications lack DOI and are therefore not included in OA statistics.
Insights
The OA share went from 100 % to 86%
(14.3 percentage points) during 1970–2026. Hybrid accounted for the
largest increase (+24.7 percentage points). Green OA accounts for 8.7 %
of all publications — available via open repository after an embargo
period (typically 6–12 months); more recent publications may not yet be
freely accessible. Diamond OA (no fees for authors or readers) accounts
for 22.5 %.
Note:
1557 of 1613 publications with DOI were
matched against OpenAlex and assigned OA status (96.5%). OA status is
sourced from OpenAlex (based on Unpaywall). OA status may be
retroactively classified — a publication that is freely available today
may have been closed at the time of publication. The trend should
therefore be interpreted with caution, especially for older
publications. Green OA classification is based on the presence of a
version in an open repository, regardless of whether any embargo period
has expired — the Green OA share may therefore be overestimated for more
recent publications.
A complete list of the search results. Initially sorted by year (descending) and author (ascending). Change the order at the column header. Search can be done over all displayed fields.
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Bornmann, L., & Marx, W (2018). Critical rationalism and the search for standard (field-normalized) indicators in bibliometrics. Journal of Informetrics, 12(3), 598–604. https://doi.org/10.1016/j.joi.2018.05.002
CoARA (2022). Agreement on Reforming Research Assessment. https://coara.eu/agreement/the-agreement-full-text/
DORA (2012). San Francisco Declaration on Research Assessment. https://sfdora.org/read/
Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520(7548), 429–431. https://doi.org/10.1038/520429a
Kleinberg, J (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373–397. https://doi.org/10.1023/A:1024940629314
Mann, H. B (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259. https://doi.org/10.2307/1907187
Neal, Z. P (2022). backbone: An R package to extract network backbones. PLOS ONE, 17(5), e0269137. https://doi.org/10.1371/journal.pone.0269137
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Opsahl, T., Agneessens, F., & Skvoretz, J (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. https://doi.org/10.1016/j.socnet.2010.03.006
Perianes-Rodriguez, A., Waltman, L., & Van Eck, N. J (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178–1195. https://doi.org/10.1016/j.joi.2016.10.006
Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., Norlander, B., Farley, A., West, J., & Haustein, S (2018). The state of OA: A large-scale analysis of the prevalence and impact of Open Access articles. PeerJ, 6, e4375. https://doi.org/10.7717/peerj.4375
Sen, P. K (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.2307/2285891
Serrano, M. Á., Boguñá, M., & Vespignani, A (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences, 106(16), 6483–6488. https://doi.org/10.1073/pnas.0808904106
Stirling, A (2007). A general framework for analysing diversity in science, technology and society. Journal of The Royal Society Interface, 4(15), 707–719. https://doi.org/10.1098/rsif.2007.0213
Traag, V. A., Waltman, L., & Van Eck, N. J (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9, 5233. https://doi.org/10.1038/s41598-019-41695-z
Van Eck, N. J., & Waltman, L (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635–1651. https://doi.org/10.1002/asi.21075
Van Eck, N. J., & Waltman, L (2014). Visualizing bibliometric networks. In Ding, Y., Rousseau, R., & Wolfram, D. (Ed.), Measuring Scholarly Impact: Methods and Practice (pp. 285–320). Springer. https://doi.org/10.1007/978-3-319-10377-8_13
Wilson, E. B (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158), 209–212.