Bifrost®: Niklas Gericke (research profile)

v. dev (8f744784)

Niemi, Kristian. (2026, 18 juni). Bifrost®-analys: Niklas Gericke (research profile). Karlstads universitet. https://bifrost.kau.se/forskning/individ/niklas_gericke__research_profile_.html

268
Publications
2026*: 4 ▼
236
Peer-reviewed
88% of total 2026*: 100% ▲
69%
Open Access
2026*: 100% ▲
+12.1%
Annual growth rate (CAGR)
Average annual growth rate: 2009–2025
40%
Level 2 (Norwegian list)
Among level-classified journals
2026*: 50% ▲
151
Researchers
2026*: 3 ▼
*Year may be incomplete
About key indicators

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.

The following query was used:
FÖRF:(Gericke, Niklas, 1970-)
Database: SwePub
● Data quality: poor
Publication activity
Publication types
Number of scientific publications per type over years
88%
Peer-reviewed
2026: 100% ▲
100%
Scientific
2026: 100% ●
76
Unique journals
2026: 4 ▼
40%
Level 2 (Norwegian list)
2026: 50% ▲
41 of 102 classified

Insights
The peer-reviewed share increased from 50 % to 100% (+50 percentage points) during 2009–2026.

267 publications (100%) scientific, 1 publications (0%) other.

Older years (2009–2018) aggregated for readability. Full timespan available in data export.

Publication types over time
Proportional view
Journals: peer reviewed and other scientific
Missing match in the Channel Register… (HK-dir). Common causes: missing ISSN in source data, channels outside the register, conference series, or recently launched journals. Lack of classification does not necessarily mean the journal lacks peer review.

FWCI (Field-Weighted Citation Impact) shows mean citation impact normalised by subject, publication type, and year. Values ≥ 1.0 indicate citations at or above the world average. FWCI is shown when at least 10 articles in the journal have data. Top 10% shows the share of articles in the top 10% most cited in their field — shown when at least 5 articles have percentile data. Median citations (hidden column) shows raw citation counts without field normalisation — not comparable across subject areas.

Publications by NPI level

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.

Method and limitations
Data source
SwePub
Time period
2009–2026
Counting method
NPI level (1 or 2) is retrieved from the Norwegian Channel Register (HK-dir) via ISSN/ISBN matching. Publications without a match are assigned level X.
Limitations
  • The channel register does not cover all scientific publishing. Publications outside the channel list lack an NPI level and are counted as level X.
  • Level-based indicators should be interpreted contextually and not used as the sole quality measure. Hicks et al. (2015)
  • NPI classification is sourced from the Norwegian channel register (HK-dir) and does not cover all scientific publishing. Publications outside the channel list have no NPI level.
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
40.2%
Level 2
2026: 50.0% ▲
41
Level 2 (Count)
2026: 2 ▼
54
Level 1 (Count)
2026: 2 ▼
166
Unclassified
61.9% of total

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.

NPI level by year
Publications without NPI classification
Method and limitations
Data source
SwePub + Kanalregisteret (HK-dir)
Time period
2009–2026
Counting method
Full counting — each publication counted as one unit
Limitations
  • Volume measures count publications, not pages published or contribution size.
  • Conference papers may be underrepresented in the source database, particularly for older periods and certain disciplines.
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
  • NPI classification is sourced from the Norwegian channel register (HK-dir) and does not cover all scientific publishing. Publications outside the channel list have no NPI level.
DORA

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

Researchers are listed below, sorted by scientific productivity.

283
Unique researchers
2026: 10 ▼
51%
Top 10 researchers’ publication share
The 10 most productive (of 283 total) researchers’ share of all publications
3.5
Co-authors/pub.
2026: 3.8 ▲

Insights
The top 20% most productive researchers account for 57% of publications (Gini 0.44, scale: 0 = even, 1 = fully concentrated). Average number of co-authors increased from 2.7 (2009–2017) to 4.0 (2018–2026).

Method and limitations
Data source
SwePub
Time period
2009–2026
Counting method
Full counting — each publication counted as one unit
Limitations
  • Lists show the most productive researchers by volume. Rankings reflect registered publishing activity, not scientific quality or impact.
  • This section is descriptive. Individual researchers are not evaluated; the measure is a group result at aggregate level.
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
Collaboration
Co-authorship

Insights
15 research groups of roughly equal size; no single cluster dominates. Each researcher collaborates with an average of 4.0 others (a moderately connected network). Clear cluster structure (Modularity 0.87); researchers primarily work within their own group. The network is sparse: only 6.0% of all researcher pairs have a direct collaboration link.

Methodology

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).

4.0
Collaborators (avg)
2009–2026
0.87
Clustering
2009–2026
Degree distribution
Summary

Percentages are calculated on pairs where both authors have country data (770 classified of 770 total, coverage 100%).

Individual network statistics

‘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.

Most common co-authorships

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.

Network of co-authors

Below is a visualization of the 15 different groupings in the dataset. The colors indicate different groups.

The network shows 67 of 283 co-authors: those who share at least 2 publication with another.

Why are not all co-authors shown?

A co-author is only included in the network once they have co-authored at least 2 publication with another. Pairs who meet in only a single publication are therefore excluded. The threshold dampens noise so recurring collaborations stand out more clearly.

Publications with more than 25 authors are excluded when building the network. These are typically meta-studies, systematic reviews, and large consortium articles where the full author list is printed. Letting them in would connect nearly everyone to nearly everyone else.

Because the network is large, weak links have also been dropped via backbone filtering (67 nodes, 133 edges in the original graph). The groups above are computed on the filtered, sparser network.

Adaptive visualization (large): 67 nodes / 133 edges → 6 nodes / 7 edges after filtering.

Group membership

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.

Group size
Citation impact per research cluster

The chart compares the field-weighted citation impact (FWCI) for the co-authorship clusters identified through network analysis. Each publication is assigned to the cluster where most of its authors belong. FWCI = 1.0 corresponds to the world average. n is the number of publications in the cluster (shown on hover).

Group keywords

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.

Method and limitations
Data source
SwePub
Time period
2009–2026
Counting method
Full counting — each publication counted as one unit
Limitations
  • A link in the network means two researchers share at least one publication in the selection; link strength indicates the number of co-authored works. Informal collaborations and unpublished projects are not visible.
  • Short time periods or small research groups produce sparse networks. Isolated nodes indicate researchers with few registered collaborations in the selection, not absence of collaboration in general.
  • Pairs sharing fewer than 2 publications are not included in the network (makeCoauthorMinPubs).
  • Publications with more than 25 authors are excluded from the co-authorship analysis (makeMaxAuthorsPerPub).
  • For networks exceeding 200 nodes or 500 edges, adaptive edge reduction is applied to the visualization (disparity filter, Serrano et al. (2009), or quantile threshold depending on size). Network statistics (centrality, cluster membership) are always computed on the full graph.
  • Whole counting: each shared publication contributes weight 1 per pair. To give each publication equal total weight regardless of author count, enable makeFractionalCounting = TRUE (per-publication 1/(N−1) weighting following Perianes-Rodriguez et al. 2016). Perianes-Rodriguez et al. (2016)
  • Association strength: AS(i,j) = w_ij / (k_i × k_j / 2m). The normalization reduces dominance of high-degree nodes. Van Eck et al. (2009)
  • Degree indicates the number of unique collaboration partners (network topology). Strength indicates total co-publication intensity (edge weights). A researcher with high Degree but low Strength has many shallow collaborations; conversely, high Strength with low Degree indicates few but intensive collaborations. Opsahl et al. (2010)
  • Cluster colors are based on co-publication patterns, not organizational affiliation. A cluster’s composite research groups may cross organizational boundaries.
  • Data character classified as large. Backbone filter, node cap, and cluster targets adjusted automatically.
  • Parameters: backbone α = 0.10, min. co-publications = 2, node cap = 120.
  • Leiden clustering: resolution parameter γ = 1.20 (deviates from default 1.0; calibrated for current corpus size).
  • International collaboration filter configured but not applied in this version (FM4 support requires per-author country data, planned for a future release).
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
Bibliometric network visualizations complement, rather than replace, expert judgment. Van Eck et al. (2014)

See also the supervisor and opponent network in the researcher section for an analysis of academic collaboration patterns beyond co-authorship.

Supervisor and opponent network

Unlike the co-authorship analysis, which maps collaboration through joint publications, this section reveals the academic networks that emerge through dissertation supervision and opposition. Supervisors and opponents active at multiple institutions form informal knowledge bridges between organizations — relationships rarely captured by traditional bibliometric measures but which can reveal important patterns in academic knowledge transfer.

Insights
10 researchers have supervised or opposed across institutional boundaries. Strongest connection: University of Antwerp – Karlstad University (Connection strength: 4). Based on 90.9% of dissertations with identifiable supervisors.

Supervisions  Oppositions
Supervisor and opponent network, figure
Supervisor and opponent network, table
Method description

The supervisor/opponent network is separate from the international collaboration map. The map is based on co-authorship between author affiliations, while supervisor/opponent relations are shown in the network below.

The network is based on supervisor and opponent relationships extracted from SwePub records. Connection strength is calculated as (number of supervisions × 2) + (number of oppositions × 1). The weighting (2:1) is a Bifrost convention reflecting that supervision is a longer and deeper collaborative relationship than opposition. The method lacks established bibliometric practice; it was developed specifically for Bifrost.

The supervisor:opponent weighting (2:1) is a Bifrost convention to reflect the supervisor’s greater role in the dissertation process. This is not established bibliometric practice.

Higher education institutions
88
Institutions
74%
Top-3 share

Insights
88 institutions contribute. Karlstads universitet dominated with 257 publications (61 %). Next largest: Högskolan Dalarna (9), University of Antwerp (9). Concentration index (HHI): 0.374.

Method and limitations
Data source
SwePub
Time period
2009–2026
Counting method
Full counting of publication appearances
Limitations
  • The count shows co-author affiliation appearances, not unique publications. A publication with three co-authors from the same institution counts three times.
  • Institution names have been harmonised against an internal name list. Unmatched variants are shown separately or excluded.
  • A total of 12 entries in the raw data were excluded from the table: 12 country/city names (geographic entities, not institutions), 1 manually verified non-institutions (known_unmapped). This is why the institution count may differ from the number of unique affiliations in the source data.
International collaboration

Overview of international collaboration based on co-authorship and affiliations in publications.

31
Collaboration countries
6 on average per year
21%
International collaboration

Insights
31 länder representerade i samarbeten. Belgien, Norge och Spanien är vanligast. 20.5 % av publikationerna har internationella medförfattare — ökning från 9 % (2009–2017) till 25 % (2018–2026).

Collaboration countries
Distribution per year
View:
Co-authorship by country

Based on co-author affiliation country.

Networks and publications, geographically

Insights:
The network comprises 39 institutions with 51 collaboration relationships. The strongest collaboration is between Dalarna University and Karlstad University (9 co-publications). Karlstad University has the most collaboration partners (33).

Period overview

The map primarily shows co-authorship between institutions. Supervisor/opponent links are shown as separate network relations and may be fewer, because only records with clear institutional affiliation can be included.

39
Institutions in network
2009–2026
51
Collaboration relationships
2009–2026
9
Co-publications: Dalarna University – Karlstad University
2009–2026
Method and limitations
Data source
SwePub + OpenStreetMap/Nominatim
Time period
2009–2026
Counting method
Full counting — each publication counted as one unit
Limitations
  • The international share is calculated only from author affiliation country. Conference location, publication country, and other metadata are not counted as international collaboration.
  • Country analysis is based on co-author affiliation country. Full counting means each country in a co-publication is counted once.
  • The network map shows collaboration relationships between institutions based on co-authored publications. Node size reflects the number of collaboration partners, edge width reflects collaboration strength (Salton’s cosine index).
  • Based on author affiliation data. Incomplete affiliation information may affect results.
Subject areas
Subject categories
Social Sciences (75%)
Dominant category
5
Subject areas (level 1)
0.40 / 1.00
Subject diversity (evenness)

Insights
Social Sciences dominates (72 %). Subject breadth has increased (2009–2026, H: 0.52 → 0.72). Moderate interdisciplinarity — research combines related subject areas. Rao-Stirling: 0.524 (where 0 = single discipline, 1 = maximum diversity). Based on 5 HSV main areas (Swedish classification). Rao-Stirling (Stirling, 2007)

Method: diversity indices

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).

Level 1
Proportional view

Proportion of total publications per year (%). Note that a publication may belong to multiple categories.

Category frequency over time
Level 2

During 2009–2026, 1.1% (3 of 268) publications lack subject classification at this level.

Proportional view

Proportion of total publications per year (%). Note that a publication may belong to multiple categories.

Subject categories level 2 (table)
Method and limitations
Data source
SwePub
Time period
2009–2026
Classification system
HSV/UKÄ (5 nivåer), OECD FoS (3 nivåer)
Counting method
Full counting per category
Limitations
  • Explore which subject categories are represented in the dataset. Note that publications usually have several categories. Therefore, it is the rule, not the exception, that the percentages together constitute more than 100%. If x is 100% and y is 15%, it means that all publications have been categorized as x, and of them, 15% have also been categorized as y
  • A publication classified under multiple subjects is counted in each category. The sum therefore exceeds the publication count — this is correct, not an error.
  • Classification may vary in precision across institutions and periods. Comparisons should be made with caution.
  • The Rao-Stirling index is computed at portfolio level (subject code shares across the entire dataset), not as a mean of per-publication RS. Absolute RS values are not directly comparable to benchmarks based on per-article calculations or other classification systems.
Keywords

Keywords that have been automatically excluded: biology
These are used in at least 70% of the publications.

HSV subject categories have been filtered from keywords

genetics; learning

502
Unique keywords
2026: 30 ▼
education (15.7%)
Top keyword
2026: photosynthesis education

Insights
Broad keyword profile — no single term dominates (HHI: 0.0068 — Herfindahl-Hirschman Index, where 0 = perfectly even distribution, 1 = one term dominates entirely). Most common is “education” appearing in 15.7 % of publications, across a total of 502.

Colors indicate frequency quantiles within this dataset.

Red: Highest frequency (15.7-12.64%); Blue: High frequency (12.64-9.58%); Green: Medium frequency (9.58-6.52%); Orange: Low frequency (6.52-3.46%); Gray: Lowest frequency (3.46-0.4%)

Method and limitations
Data source
SwePub
Time period
2009–2026
Counting method
Full counting — each publication counted as one unit
Limitations
  • Keywords are a mix of author-assigned and automatically generated terms. Indexing consistency varies across sources and time periods.
  • English keywords often dominate. Publications in Swedish or other languages are therefore often underrepresented in frequency analyses.
  • Keywords occurring in more than 70% of all publications are automatically excluded to prevent generic terms from dominating the analysis.
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
Keyword Insights

New and emerging themes

Comparison of relative frequency (share of publications) between periods 2009–2023 and 2024–2026.

Deep-dive analysis is shown for keywords with at least 6 publications in the focus period. The threshold follows Pearson correlation’s requirement of at least four degrees of freedom (n‑ 2 ≥ 4).

A growing trend may indicate increased research interest, but can also reflect terminological shifts.

New keywords (absent 2009–2023): physics, teaching strategies, biology education, early childhood education, education computing

Rapidly growing keywords – deep dive

The following keywords show strong growth compared to the base period, which may indicate increasing research interest.

powerful knowledge (+435%, n=9)

Growth period: 2021–2025

Driving actors (growth) (growth period 2021–2025):

Researchers: Gericke, Niklas (6), Deng, Zongyi (2), Eriksson, Anders (2)

Institutions: Karlstads universitet (11), University College London (4), Finland (1)

Co-occurring keywords: biology, transformation, subject-specific education

Last 3 years: 7 publications

Analysis based on a limited number of publications (n = 9). Results should be interpreted with caution.

subject-specific education (+585%, n=13)

Growth period: 2022–2025

Driving actors (growth) (growth period 2022–2025):

Researchers: Gericke, Niklas (10), Olsson, Daniel (3), Eriksson, Anders (2)

Institutions: Karlstads universitet (22), Linköpings universitet (2), University College London (2)

Co-occurring keywords: ämnesdidaktik, biology, powerful knowledge

Last 3 years: 10 publications

Analysis based on a limited number of publications (n = 13). Results should be interpreted with caution.

action competence (+257%, n=11)

Growth period: 2022–2025

Driving actors (growth) (growth period 2022–2025):

Researchers: Gericke, Niklas (9), Olsson, Daniel (8), Boeve-de Pauw, Jelle (3)

Institutions: Karlstads universitet (26), Utrechts universitet (5), University of Antwerp (2)

Co-occurring keywords: biology, education for sustainable development, education

Last 3 years: 7 publications

Analysis based on a limited number of publications (n = 11). Results should be interpreted with caution.

ämnesdidaktik (+399%, n=13)

Growth period: 2017–2025

Driving actors (growth) (growth period 2017–2025):

Researchers: Gericke, Niklas (12), Olsson, Daniel (4), Eriksson, Anders (2)

Institutions: Karlstads universitet (26), Cyperns ministerium för utbildning, kultur, sport och ungdom (2), Linköpings universitet (2)

Co-occurring keywords: subject-specific education, biology, education for sustainable development

Last 3 years: 9 publications

Analysis based on a limited number of publications (n = 13). Results should be interpreted with caution.

sustainability education (+2040%, n=6)

Growth period: 2024–2026

Driving actors (growth) (growth period 2024–2026):

Researchers: Gericke, Niklas (4), Eriksson, Anders (3), Olsson, Daniel (2)

Institutions: Karlstads universitet (9), Högskolan Dalarna (2)

Co-occurring keywords: biology, photosynthesis education, powerful knowledge

Last 3 years: 5 publications

Analysis based on a limited number of publications (n = 6). Results should be interpreted with caution.

Showing the 5 keywords with strongest statistical evidence out of 22 identified. Selection is based on significance (p < 0.05) and correlation strength (Kendall’s tau).

Other growing keywords (lower statistical evidence): science education (+756%, n=6), secondary school (+756%, n=6), teaching practices (+756%, n=6), sustainability (+328%, n=6), contagion literacy (+114%, n=6), covid-19 (+114%, n=6), health literacy (+71%, n=7), climate change education (+1184%, n=4), photosynthesis education (+1184%, n=4), genetics education (+756%, n=3), secondary education (+756%, n=3), environmental education (+542%, n=5), preschool (+328%, n=4), scientific literacy (+328%, n=4), students (+328%, n=4), teacher beliefs (+328%, n=4), delphi study (+185%, n=5)

Method and limitations
Data source
SwePub
Time period
2009–2026
Counting method
Burst detection uses an automaton model implemented via the bursts package. The method identifies periods of statistically significant increased occurrence of individual keywords. Kleinberg (2003)
Limitations
  • Burst detection requires at least 5–10 years of data for reliable results. Short time series may produce unstable or misleading burst periods.
  • Keywords are a mix of author-assigned and automatically generated terms. Indexing consistency varies across sources and time periods.
  • English keywords often dominate. Publications in Swedish or other languages are therefore often underrepresented in frequency analyses.
  • Keywords occurring in more than 70% of all publications are automatically excluded to prevent generic terms from dominating the analysis.
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
Keyword co-occurrence

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).

The heatmap shows co-occurrence strength for all pairwise combinations of the most frequent keywords, including weak relations. It is a complete N×N matrix. The keyword network below complements this view: it shows cluster structure through backbone filtering that hides weak edges to emphasize strong patterns — the two visualizations measure the same thing but illuminate different aspects.
Top co-occurring keyword pairs
Method and limitations
Data source
SwePub
Time period
2009–2026
Counting method
Co-occurrence matrix strength is computed using association strength: c_ij / (s_i × s_j / 2m), where s is document frequency and m is the sum of pairwise co-occurrences. Van Eck et al. (2009)
Limitations
  • Association strength is normalized following Van Eck & Waltman (2009): AS(i,j) = c_ij / (c_i × c_j / 2m), where c_ij is the document frequency for the pair, c_i and c_j are the document frequencies for the individual keywords, and m is the sum of co-occurrences over unique pairs (upper triangle of the co-occurrence matrix). Van Eck et al. (2009)
  • Co-occurrence is counted per document (whole counting). Fractional counting is not applied at the keyword level — a deliberate choice because the keywords are controlled terms (SwePub) or extracted concepts (OpenAlex), not free-text author names.
  • The heatmap displays the 15 most frequent keywords (ranked by document frequency). Keywords below the minimum frequency threshold are excluded. The count can be adjusted in the report configuration.
  • Statistical significance testing uses the hypergeometric distribution at the pair level. No correction for multiple testing is applied — the analysis is exploratory, not confirmatory.
  • The heatmap remains readable up to top_n ≤ 25. For larger datasets — see also the keyword network.
  • The heatmap color scale is clipped at the 95th percentile of observed association strengths. Association strength with 2m rescaling is mathematically unbounded: rare keyword pairs with low individual document frequencies can produce extreme values that otherwise dominate the scale and render other cells invisible. Actual maximum values are shown in the top-pairs table.
  • Graph-based visualizations degrade for large networks (Van Eck & Waltman, 2014, pp. 288–289). The heatmap is designed for top-N pairs; the network graph uses backbone filtering to handle larger networks. Van Eck et al. (2014)
  • Keywords are a mix of author-assigned and automatically generated terms. Indexing consistency varies across sources and time periods.
  • English keywords often dominate. Publications in Swedish or other languages are therefore often underrepresented in frequency analyses.
  • Keywords occurring in more than 70% of all publications are automatically excluded to prevent generic terms from dominating the analysis.
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
Network diagram, keywords

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 shows cluster structure and network position: backbone filtering retains statistically significant edges and hides weak relations to emphasize strong patterns. The co-occurrence heatmap above complements this view: it shows the full N×N matrix for the most frequent keywords, including weak pairs not visible in the network.

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).

Cluster overview
Method and limitations
Data source
SwePub
Time period
2009–2026
Counting method
Co-occurrence matrix strength is computed using association strength: c_ij / (s_i × s_j / 2m), where s is document frequency and m is the sum of pairwise co-occurrences. Van Eck et al. (2009)
Limitations
  • Adaptive filtering (scale: large): backbone α=0.05, min co-occurrence=3. Nodes 40→17, edges 31→18, density=0.132.
  • The SDSM filter tests each keyword pair against a stochastic null model that controls for both keyword frequency and the number of keywords per publication. Edges that do not deviate significantly are removed. Neal (2022)
  • Clusters are detected using the Leiden algorithm Traag et al. (2019), which identifies thematic groups where keywords co-occur more strongly within the group than between groups.
  • The network is limited to the most frequent keywords (top N). Rare keywords are excluded, which may hide emerging topics.
  • Filter parameters are selected by corpus size. Small corpora (< 50 publications or < 20 unique keywords) get no backbone filter. Medium (50–299 publications with 20–149 keywords) use backbone α=0.15 and 60 keywords. Large (≥ 300 publications or, at ≥ 50 publications, ≥ 150 keywords) use α=0.05 and 40 keywords. As an exception, small publication sets with rich vocabulary (< 50 publications but ≥ 150 keywords — typically OpenAlex individual reports) use the medium operating point rather than large, because the aggressive filter otherwise fragments the network. Thresholds are empirical operating points and can be overridden per report.
  • Graph-based layouts degrade for large networks (Van Eck et al., 2014). Above 200 nodes, visual clarity depends on backbone filtering and node reduction. Statistics (centrality, cluster membership) are always computed on the complete graph.
  • The number of keywords in the network is adapted to the material: roughly 30 % of the unique keywords are included, with a floor of 60 and a ceiling of 250. Of these, roughly 65 % (at least 30, at most 70 nodes) are shown initially to keep the graph readable. The remaining keywords can be added via the slider below the graph. These limits prevent both sparse and overloaded networks.
  • Keywords are a mix of author-assigned and automatically generated terms. Indexing consistency varies across sources and time periods.
  • Keywords in Swedish and English are mixed without separation. Lemmatization is not applied: ‘learning’ and ‘learners’ are treated as separate keywords. This is a deliberate choice for controlled terms, not a deficiency — but it affects the concentration around English-language concepts.
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
Methodology: adaptive visualization
Data character
manual
Filter parameters
α=0.05, top_n=40, min_cooc=3
Before filtering
40 nodes, 31 edges
After filtering
17 nodes, 18 edges
Edge density
0.132
Quality gate
Not triggered
Word frequency

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.

3 841
Unique words
biology (75.4%)
Top word

Insights
The 5 most common words (by share of publications) are: “biology” (75%), “education” (69%), “school” (45%), “students” (41%), “teaching” (41%). These patterns reflect the thematic core of the dataset.

Word frequency table

If you want to examine the frequency of some specific words more closely, enter them in the variable ‘to_stem’.

Method and limitations
Data source
SwePub
Time period
2009–2026
Counting method
Full counting — each publication counted as one unit
Limitations
  • Word frequencies are derived from titles and abstracts in the dataset. Swedish and English stop words are filtered before calculation.
  • The analysis is language-dependent and does not merge synonymous terms across languages. No stemming or lemmatization is applied — word forms are counted separately.
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
Word Frequency Trends

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.

Impact and accessibility
Citations

The analysis below summarises citations in the dataset and highlights trends over time, by researcher, and for the most cited works.

The publication information has been retrieved from SwePub and enriched with citation data from OpenAlex.

The annual publication count has changed positively over the period 2009–2025 (CAGR based on 16 complete calendar years, current year excluded). This analysis covers 268 publications from SwePub for the period 2009–2026. Citation data is available for 44 % of publications.

Generated from period data

Note:
Citation data are cumulative totals retrieved from OpenAlex — they show how many times each publication has been cited since it was published. Publications from more recent years have had less time to accumulate citations, which should be considered when comparing across years.

On comparability:
Field-normalized percentiles from OpenAlex (normalized by year, work type, and subfield). 29 publications (11%) are from the last 2 years and may have understated percentiles.

Self-citation
Citation counts in this report include both external citations and self-citations. The self-citation share may be substantial for individuals and small datasets. See the method card below for details. Hicks et al. (2015)

About FWCI (citation impact)

FWCI (Field-Weighted Citation Impact) measures how much a publication has been cited compared to what is expected for that type of research, publication year, and subject field, internationally. FWCI = 1.0 is the expected value: the typical number of citations for similar publications globally. Below 1.0 means fewer citations than expected; 1.5 means 50% more; 2.0 means twice as many. Normalisation by field is necessary because citation cultures differ markedly. Medicine cites far more frequently than mathematics, making direct comparisons misleading. Data comes from OpenAlex. Note that a few highly cited publications can pull the figure up substantially, and the measure requires sufficient coverage (at least 10 publications with citation data).

About PP(top 10%), citation impact

PP(top 10%) measures the share of a group’s publications that rank among the top 10% most cited in their subject field and publication year, internationally. The reference value is 10%: if a group published at a perfectly average level, exactly 10% would fall into the top bracket. Above 10% means a larger share than expected achieves high citation impact; below 10% means the opposite. The measure is field-normalised, meaning each publication is compared with others in the same field and year. This avoids the problem that, for example, medical research is generally cited more than mathematics. Data comes from OpenAlex. Note that small datasets can produce large random fluctuations, and recently published articles often lack sufficient citation history for a fair ranking.

Citation coverage:
Citation data could only be retrieved for 47% of publications (127 of 268). Citation indicators should be interpreted with great caution as they represent a small portion of the dataset.

Citations show how often other researchers reference these publications in their own work. High citation counts indicate that the research has had impact within its field.

44%
Citation coverage
118 / 268
96.8
Median percentile (field-norm.)
Top 1%: 29.7%
⚠ 29 publ. < 2 yrs
78.8%
Top 10%
Complementary metrics

FWCI (Field-Weighted Citation Impact) is the ratio of actual to expected citations, normalised by year, work type, and subject field (OpenAlex subfield). FWCI = 1.0 means the publications are cited in line with the world average for their field. Unlike percentile measures, FWCI is sensitive to individual highly cited publications — two units with the same PP(top 10%%) may differ in FWCI if one has a few very highly cited works.

Mean FWCI: 14.91

Based on 118 publications with FWCI data

Stability intervals (BCa bootstrap)

The stability interval (95%) indicates the likely range of values if the publication set were to change. Computed via BCa bootstrap (bias-corrected and accelerated) at the publication level with 2,000 replicates. Terminology and confidence level follow the CWTS Leiden Ranking; the BCa variant (rather than simple percentile bootstrap) is methodologically stronger. Not shown when the underlying data are too sparse (FWCI: n < 10; PP(top 10%) and PP(top 1%): n < 30) or when percentile coverage is low (< 50%). Waltman et al. (2012), DiCiccio et al. (1996)

FWCI: 14.91 (uncertainty interval 11.57–20.13)

No stability interval for Top 10%: percentile coverage 44.0% (requires at least 50%).
No stability interval for Top 1%: percentile coverage 44.0% (requires at least 50%).

Citation profile — percentile distribution over time

The chart shows the proportion of publications in different citation percentile bands per year, based on field-normalized percentiles from OpenAlex. Publications from the most recent 2 years are excluded due to incomplete citation accumulation.

Citation impact by Open Access status

The chart compares the average field-weighted citation impact (FWCI) for publications with different Open Access statuses. The reference line marks the world average (FWCI = 1.0). FWCI requires at least 10 publications per category.

Citation impact by collaboration type

The chart compares citation impact for publications with international collaboration, domestic collaboration, and single-author publications. International collaboration is defined as publications with authors from more than one country.

Citation distribution by year

The boxplot shows the distribution of field-weighted citation impact (FWCI) per publication year. The dashed line marks the world average (1.0). Publications from the most recent 2 years are excluded.

Insights
264 publications have a total of 4 691 citations (median 0.0/publication, avg 17.8/publication). 56.4 % are uncited. Most cited (312 cit): Gericke, Niklas;Boeve-de Pauw, Jelle;Berglund, Teresa;Olsson, Daniel (2018). The Sustainability Consciousness Questionnaire: The theoretical development and empirical validation of an evaluation instrument for stakeholders working with sustainable development. Sustainable Development. https://doi.org/10.1002/sd.1859 The field-weighted citation impact (FWCI) averages 13.21 (based on 96 publications). The trend is decreasing.

Citations over time: most cited publications

The chart shows how citations are distributed across publication years. Note that older publications have had more time to accumulate citations.

Grey bars mark publications from the last two years, whose citation data are incomplete — they have not had time to accumulate citations to the same extent as older publications.

Citation impact per researcher over time

The chart shows the field-weighted citation impact (FWCI) for the most cited researchers, with a three-year rolling average. The dashed line marks the world average (FWCI = 1.0). Includes researchers with at least 5 publications spanning at least 3 years.

Most cited publications
Method and limitations
Data source
SwePub + OpenAlex (retrieved 2026-06-18)
Time period
2009–2026
Citation measure
Field-normalized percentile (0–100), PP(top 10%), PP(top 1%)
Counting method
Not applicable — one citation count per publication
Limitations
  • Median is used as the primary central tendency measure because citation distributions are heavily skewed. Mean is shown as supplementary context.
  • Recent publications have had less time to accumulate citations (fallow period effect). Lower counts for recent publications are expected and do not indicate lower quality.
  • Field normalization uses OpenAlex percentiles, calculated against year, work type, and OpenAlex algorithmic subject classification (~250 subfields). This classification is not identical to HSV/UKÄ or OECD FoS — it is based on automated clustering, not manual classification. Percentiles are therefore not directly comparable with the subject distribution in other sections of the report.
  • OpenAlex coverage varies across fields. Humanities and national journals are underrepresented, which may affect percentile values.
  • OpenAlex percentiles are recalculated continuously. Values may change retroactively when the database is updated.
  • Full counting is used: each publication is counted once regardless of the number of co-authors. For units with high collaboration rates, this may yield higher citation figures than fractional counting. Fractional counting is not feasible with available API data.
  • Self-citations are not separated — citation counts include both external and self-citations. For small datasets, the self-citation share may be substantial. Reporting self-citations separately is highlighted as good practice in Leiden Manifesto principle 9 on open and contextualised indicator use. Exclusion requires each citing work to be checked against the publication’s authors — a separate analysis step not implemented in the current pipeline. Hicks et al. (2015); Waltman et al. (2019)
  • Citation counts are not time-adjusted: older publications have had more time to accumulate citations. Direct comparison of citation counts across publication years should be interpreted with caution. Adams (2018)
  • Citation-based time series are Type 1 (diachronous): more recent publication years have shorter citation windows, creating a structural decline at the end of the series — not necessarily a real change. Adams (2018)
  • The indicators in this section are citizen bibliometrics — suitable for internal comparisons within the same field but not for cross-disciplinary comparisons without field normalization. Bornmann et al. (2018)
Open Access
47%
Open Access
2026: 100% ▲
9%
Green OA
2009–2026
+100 p.p.
OA change
2026: 100%
2009: 0% 2009–2026

The OA analysis is based on 127 publications with DOI matched against OpenAlex (47% of 268 total). 141 publications lack DOI and are therefore not included in OA statistics.

Open Access category definitions
  • Gold OA: Published in a fully open access journal (typically with an article processing charge).
  • Green OA: Freely available via an open repository (e.g. institutional repository), typically after an embargo period of 6–12 months, even if the journal is not open access.
  • Hybrid: Published as an open article in an otherwise subscription-based journal (typically with an APC).
  • Bronze: Freely readable on the publisher’s website but without a clear open license (may be removed). Not counted in the OA share because it lacks a formal open license (BOAI/Berlin Declaration).
  • Diamond: Published in a journal that is fully open with no author-facing charges (APC). Often funded by institutions or organizations.
  • Closed: Not freely available — requires subscription or purchase.

Insights
The OA share went from 0 % to 100% (+100 percentage points) during 2009–2026. Hybrid accounted for the largest increase (+39 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 3.9 %.

Open Access types over time
Open/closed per year (absolute)

Note:
127 of 127 publications with DOI were matched against OpenAlex and assigned OA status (100%). 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.

Method and limitations
Data source
SwePub + OpenAlex (Unpaywall) Piwowar et al. (2018)
Time period
2009–2026
Counting method
Full counting — each publication counted as one unit
Limitations
  • OA status is sourced from OpenAlex (based on Unpaywall) and may differ from the publisher’s current status. Retroactive changes to OA status are not always captured.
  • The Green OA time series shows the current proportion per publication year — not when the article actually became openly available. Retroactive self-archiving (backfilling) means older years may show higher Green OA shares than at the time of publication.
  • OA data is sourced from OpenAlex/Unpaywall. Coverage is incomplete — actual OA share may be higher than reported, especially for older publications and material archived in systems outside Unpaywall.
  • Bronze OA (freely readable without an open license) is excluded from the OA share since Bifrost v0.8.0, in accordance with the BOAI/Berlin Declaration requirement for an open license. Comparisons with reports generated by older versions may show lower OA shares for the same period. Bronze is still shown in charts and tables.
  • Confidence intervals for OA proportions are computed using the Wilson score method, which provides reliable intervals even for small samples. Wilson (1927)
Publications
Theses

The first doctoral thesis in the dataset is from 2009, Science versus School-science: Multiple models in genetics - The depiction of gene function in upper secondary textbooks and its influence on students’ understanding by Gericke, Niklas. From then until 2025, a total of 11 theses have been registered. Of these, 7 are doctoral theses and 4 are licentiate theses.

11
Theses
2026: 0 ▼
Supervisors
Opponents
Method and limitations
Data source
SwePub
Time period
2009–2025
Counting method
Full counting — each publication counted as one unit
Limitations
  • Thesis data is sourced from the selected data source. Information on type, supervisor, and opponent depends on how the registering institution has entered the data.
  • Coverage may be incomplete: theses not registered in the data source are not visible in the analysis. Historical theses are often underrepresented.
  • Data reflects publishing activity retrieved from SwePub and may differ from the institution’s internal statistics.
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.

Download RIS file Download BibTeX file Download Excel file


Method references

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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/

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DORA (2012). San Francisco Declaration on Research Assessment. https://sfdora.org/read/

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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

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