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 1995-2025, the
compound annual growth rate (CAGR) was +13.4%. The trend is
increasing (statistically significant; p < 0.05).
Mann
(1945);
Sen
(1968)
Insights
The peer-reviewed share: 86.1 % recent
decade (2017–2026), up from 81.5 % previous decade (2007–2016).
Long-term trend (1995–2026): increasing. Note that 0% in the first year
of the period may reflect incomplete metadata rather than an actual
absence of peer review.
1142 publications (100%) scientific, 3 publications (0%) other.
Older years (1995–2018) 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 29% of publications (Gini 0.11, scale: 0 = even, 1 = fully
concentrated). Average number of co-authors: 7.4 recent decade
(2017–2026), up from 4.6 (2007–2016).
Insights
74 research groups of roughly equal
size; no single cluster dominates. Each researcher collaborates with an
average of 5.1 others (a densely connected network). Clear cluster
structure (Modularity 0.98); researchers primarily work within their own
group. The network is sparse: only 0.8% 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 (2523 classified of 2566 total, coverage 98%). Of which 18 pairs where both authors lack institutional affiliation, 0 pairs where the institution could not be mapped to a country, and 25 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 74 different groupings in the dataset. The colors indicate different groups.
The co-authorship network comprises 509 researchers and 1029 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.
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
35 researchers have supervised or
opposed across institutional boundaries. Strongest connection: Karlstads
universitet – Uppsala universitet (Connection strength: 4). Based on 66%
of dissertations with identifiable supervisors.
Rangordningen är inte tillförlitlig på grund av ofullständig data. Listan visas i alfabetisk ordning.
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.
Insights
710 institutions contribute. Uppsala
University, Stockholm University and Swedish University for Agricultural
Sciences account for 22 % — a broad distribution.
Overview of international collaboration based on co-authorship and affiliations in publications.
Insights
78 countries represented in
collaborations. Sverige, Storbritannien and United States are most
common. 25.2 % of publications involve international co-authors — up
from 10 % (2007–2016) to 37 % (2017–2026).
Based on co-author affiliation country.
Insights:
The network comprises 273 institutions
with 1047 collaboration relationships. The strongest collaboration is
between University of Reading and Uppsala universitet (29
co-publications). Uppsala universitet has the most collaboration
partners (120).
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.
Insights
Natural sciences dominates (82 %).
Subject breadth has increased — research has become more diversified
(1995–2026, H: 0.57 → 0.76). Moderate interdisciplinarity — research
combines related subject areas. Rao-Stirling: 0.547 (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 30 level 2 categories in the dataset. Showing the 25 most frequent here.
Proportion of total publications per year (%). Note that a publication may belong to multiple categories.
During 1995–2026, 18.4% (211 of 1,145) publications lack subject classification at this level.
Proportion of total publications per year (%). Note that a publication may belong to multiple categories.
geovetenskap; miljökemi; skogsskötsel; remote sensing; fiske; skogsteknik; fjärranalys; lantmäteri; vatten i natur och samhälle; exogen geovetenskap; övrig geovetenskap; geoteknik; miljöteknik; lärande; geovetenskap(ersätts med naturgeografi); morfologi; atmosfärs- och hydrosfärsvetenskap; medicin; learning; design (overall design); mathematics
Insights
Broad keyword profile — no single term
dominates (HHI: 0.0011 — Herfindahl-Hirschman Index, where 0 = perfectly
even distribution, 1 = one term dominates entirely). Most common is
“climate change” appearing in 7 % of publications, across a total of
3217.
Colors indicate frequency quantiles within this dataset.
Red: Highest frequency (7-5.62%); Blue: High frequency (5.62-4.24%); Green: Medium frequency (4.24-2.86%); Orange: Low frequency (2.86-1.48%); Gray: Lowest frequency (1.48-0.1%)
Declining keywords: hydrology, earth sciences, hydrologi
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: 2006–2011 (moderate burst)
Peak year: 2007 (9 pubs.)
Driving actors during period:
Co-varying keywords: uncertainty, flood risk, sequence stratigraphy
Current status: Declining
Burst period: 2016–2021 (moderate burst)
Peak year: 2020 (8 pubs.)
Driving actors during period:
Co-varying keywords: droughts, hydrology, climate change
Current status: Stable
Burst period: 2016–2020 (moderate burst)
Peak year: 2017 (7 pubs.)
Driving actors during period:
Co-varying keywords: den föränderliga jorden, lena river, amphibole microchemistry
Current status: Stable
Burst period: 2016–2020 (moderate burst)
Peak year: 2017 (5 pubs.)
Driving actors during period:
Co-varying keywords: the changing earth, lena river, apollo 14
Current status: Stable
Burst period: 2017–2020 (moderate burst)
Peak year: 2019 (3 pubs.)
Driving actors during period:
Co-varying keywords: mediterranean, past millennium, stable isotopes
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: “flood” (71%), “water” (45%), “flooding” (39%),
“climate” (35%), “risk” (30%). These patterns reflect the thematic core
of the dataset.
Notice: The dataset contains 19467 rows. For best performance, only the 8000 with the highest frequency are displayed in the table.
If you want to examine the frequency of some specific words more closely, enter them in the variable ‘to_stem’.
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 841 publications with DOI matched against OpenAlex (73% of 1145 total). 245 publications lack DOI and are therefore not included in OA statistics.
Insights
The OA share went from 0 % to 50% (+50
percentage points) during 1996–2026. Gold accounted for the largest
increase (+28.7 percentage points). Green OA accounts for 7.5 % 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 3.4
%.
Note:
841 of 900 publications with DOI were
matched against OpenAlex and assigned OA status (93.4%). 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.
The first doctoral thesis in the dataset is from 1995, Forests and Water - Friends or Foes?: Hydrological implications of deforestation and land degradation in semi-arid Tanzania by Sandström, Klas. From then until 2025, a total of 82 theses have been registered. Of these, 69 are doctoral theses and 13 are licentiate theses.
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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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
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