How Many Authors per Manuscript?
The 1,015 manuscripts in IEEE Access Volume 14 (2026) generated 3,918 individual author records — a mean of 3.86 authors per paper. The distribution is unimodal and right-skewed: the modal value is 3 authors (233 papers, 22.9%), the median is 4, and the interquartile range spans 2 to 5.
Small collaborative teams are the clear norm. Papers with 2–4 authors account for 641 manuscripts — 63.2% of the entire volume. Solo-authored papers represent 6.0% (61 papers), while papers with 5 or more authors make up 25.1%. At the extreme, one paper lists 14 authors, a signature of large multi-institutional or interdisciplinary consortium work.
"63% of all papers come from teams of just 2–4 authors — compact collaboration is the norm, not the exception, in IEEE Access 2026."
Distribution of authors per manuscript
Figure 1 — Histogram of authors per manuscript. Red bar = mode (3 authors, 22.9%). Dashed orange line = mean (3.86). Source: author_name field, raw dataset.
Complete frequency table
| Authors per Paper | Manuscripts | Share (%) | Cumulative (%) |
|---|---|---|---|
| 1 author | 61 | 6.0% | 6.0% |
| 2 authors | 208 | 20.5% | 26.5% |
| 3 authors ▲ MODE | 233 | 23.0% | 49.5% |
| 4 authors | 206 | 20.3% | 69.8% |
| 5 authors | 123 | 12.1% | 81.9% |
| 6 authors | 87 | 8.6% | 90.4% |
| 7 authors | 49 | 4.8% | 95.3% |
| 8 authors | 24 | 2.4% | 97.6% |
| 9 authors | 6 | 0.6% | 98.2% |
| 10 authors | 11 | 1.1% | 99.3% |
| 11 authors | 4 | 0.4% | 99.7% |
| 12 authors | 2 | 0.2% | 99.9% |
| 14 authors | 1 | 0.1% | 100.0% |
| Total | 1,015 | 100.0% | — |
Which Subjects Are Most Published?
subject_categories field with zero renaming, grouping, or reclassification.
All 67 distinct categories are preserved exactly as provided. Papers can carry multiple category
tags; counts reflect how many papers were assigned each tag.
Computational and artificial intelligence dominates the volume by a wide margin — appearing in 467 papers (46.0% of all manuscripts). This isn't a marginal lead: the second-ranked category, Computers and information processing, appears in 306 papers (30.1%). The gap between first and second is larger than the gap between second and eighth place.
The data confirms that IEEE Access Volume 14 is, in practice, primarily a journal of applied AI and computing research. Power engineering (132), signal processing (128), and communications technology (122) form a coherent second tier, reflecting the journal's traditional strength in electrical engineering. Biomedical Engineering (67) and Robotics and automation (60) represent the applied life-science and automation communities.
Figure 2 — Subject category frequency. Labels are verbatim from the dataset's subject_categories field. Each bar shows the number of papers tagged with that category.
| # | Subject Category (original label) | Papers | Share of 1,015 |
|---|---|---|---|
| #1 | Computational and artificial intelligence | 467 | 46.0% |
| #2 | Computers and information processing | 306 | 30.1% |
| #3 | Power engineering and energy | 132 | 13.0% |
| #4 | Signal processing | 128 | 12.6% |
| #5 | Communications technology | 122 | 12.0% |
| #6 | Industry applications | 114 | 11.2% |
| #7 | Imaging | 101 | 10.0% |
| #8 | Control systems | 92 | 9.1% |
| #9 | Power electronics | 90 | 8.9% |
| #10 | Circuits and systems | 85 | 8.4% |
| #11 | Sensors | 79 | 7.8% |
| #12 | Antennas and propagation | 71 | 7.0% |
| #13 | Vehicular and wireless technologies | 67 | 6.6% |
| #14 | Biomedical Engineering | 67 | 6.6% |
| #15 | Robotics and automation | 60 | 5.9% |
| #16 | Systems engineering and theory | 59 | 5.8% |
| #17 | Microwave theory and techniques | 54 | 5.3% |
| #18 | Reliability | 53 | 5.2% |
| #19 | Instrumentation and measurement | 42 | 4.1% |
| #20 | Intelligent transportation systems | 41 | 4.0% |
Where in the World Are Authors Publishing From?
author_affiliation entry — the position where country names conventionally appear.
Only clear abbreviation variants were normalised (e.g., "Republic of Korea" → South Korea;
"U.K." → United Kingdom; "Türkiye" → Turkey). Each country is counted once per paper regardless
of how many authors share it. 91 unique countries are represented.
China (182 papers) and South Korea (170) lead by a clear margin, together present in over one-third of all manuscripts. India (115) takes a strong third. The USA (84) and Japan (67) complete the top five.
What stands out is the breadth: 91 countries are represented — a genuinely global journal. Saudi Arabia (52) and Turkey (43) rank sixth and seventh, reflecting rapidly growing engineering research ecosystems in both nations. Among European contributors, Italy (29), Germany (27), the United Kingdom (27), and Spain (26) feature prominently.
Three phases of global contribution
China (182), South Korea (170), India (115), and Japan (67) together account for 53% of all identified manuscripts — a clear reflection of the region's engineering research output.
USA (84), Brazil (41), Saudi Arabia (52), Turkey (43), and key European nations together contribute meaningfully across engineering and computing disciplines.
Beyond the top 20, 71 more countries appear in the dataset — from Kazakhstan and Estonia to Tanzania and Fiji — confirming IEEE Access's genuinely global reach.
Figure 3 — Top 20 countries by paper count. Each country is counted once per paper even if multiple authors share that country. Source: author_affiliation field, raw data.
| # | Country | Papers | % of 1,015 |
|---|---|---|---|
| #1 | China | 182 | 17.9% |
| #2 | South Korea | 170 | 16.7% |
| #3 | India | 115 | 11.3% |
| #4 | USA | 84 | 8.3% |
| #5 | Japan | 67 | 6.6% |
| #6 | Saudi Arabia | 52 | 5.1% |
| #7 | Turkey | 43 | 4.2% |
| #8 | Brazil | 41 | 4.0% |
| #9 | Italy | 29 | 2.9% |
| #10 | Taiwan | 28 | 2.8% |
| #11 | Pakistan | 28 | 2.8% |
| #12 | Germany | 27 | 2.7% |
| #13 | United Kingdom | 27 | 2.7% |
| #14 | Malaysia | 26 | 2.6% |
| #15 | Spain | 26 | 2.6% |
| #16 | United Arab Emirates | 20 | 2.0% |
| #17 | Iran | 19 | 1.9% |
| #18 | Egypt | 18 | 1.8% |
| #19 | Poland | 17 | 1.7% |
| #20 | Canada | 15 | 1.5% |
Corrected Funding Breakdown
After reclassification, the 1,015 manuscripts split into three meaningful groups: those with genuine external funding, those where the only "funding" was institutional APC payment, and those with no funding entry at all.
Figure 4 — Three-way corrected split. The raw field count of 707 non-null entries yields a misleading 69.7% funded figure; the corrected external funding rate is 51.2%.
| Category | Manuscripts | Percentage | Description |
|---|---|---|---|
| Externally Funded | 520 | 51.2% | Proper government / agency / international programme funding |
| Institutional / APC-only | 187 | 18.4% | University paid own APC or listed internal grant only; no external agency |
| No Funding Mentioned | 308 | 30.3% | funding_agency field is completely blank |
| Total self-funded (rows 2+3) | 495 | 48.8% | No independent external funder identified |
| Grand total | 1,015 | 100.0% |
Which Agencies Actually Funded the Most Papers?
The following analysis covers only the 520 genuinely externally funded papers. Agency names are recorded verbatim from the dataset; where the same body appears under multiple name variants, entries have been counted separately. The NSFC, NRF, and JSPS dominate — perfectly consistent with China, South Korea, and Japan's top-three publishing positions.
Figure 5 — Top external funding agencies. Institutional/APC entries are excluded. Counts reflect number of externally funded papers citing each agency.
| # | Funding Agency (as in dataset) | Papers |
|---|---|---|
| #1 | National Natural Science Foundation of China (NSFC) | 30 |
| #2 | National Research Foundation of Korea (NRF) | 13 |
| #3 | Korean Government (MSIT) | 12 |
| #4 | Japan Society for Promotion of Science (JSPS) KAKENHI | 10 |
| #5 | National Key R&D Program of China | 7 |
| #6 | NRF grant funded by Korean Govt. (MSIT) | 7 |
| #7 | JSPS KAKENHI (various forms) | 6 |
| #8 | Icelandic Research Fund | 5 |
| #9 | National Science Foundation (NSF, USA) | 5 |
| #10 | IITP (Korea) | 5 |
| #11 | National Science Centre of Poland | 4 |
| #12 | National Science & Technology Council, Taiwan | 4 |
| #13 | CNPq – Brazil | 4 |
| #14 | NRF grant (Korea) | 4 |
What This Tells Us About Open Access in 2026
The IEEE Access Volume 14 dataset offers a rare ground-level view of how open access actually works in practice — not the idealised version, but the messy, self-reported, institutionally complex reality. Several things stand out.
The APC model creates incentives for ambiguous reporting. When authors are required to name a funding body but their institution simply paid the fee, many enter the institution rather than leave the field blank. This is not fraud — it is a rational response to a form that wasn't designed for their situation. But it produces statistics (like "69.7% funded") that mislead downstream readers and policymakers.
Nearly half of all 2026 papers have no external grant backing. At 48.8%, the self-funded share is not a rounding error. It means that roughly one in two papers in this volume was produced by researchers who either funded their own APC institutionally or received no dedicated research grant support at all. This challenges the assumption that open access mandates automatically unlock a funded author base.
Geography and funding are tightly correlated. The top external funders — NSFC (China), NRF (South Korea), JSPS (Japan) — map almost perfectly onto the top publishing countries. Where national research infrastructure is strong, publication volume follows. Where it is weaker — as suggested by the large self-funded and APC-only share — output may still reach publication but without the resource base that typically underpins high-impact research.
"A journal accepting 1,015 papers in a single volume quarter, from 91 countries, on 67 subject areas — yet nearly half without a single external grant. IEEE Access in 2026 is a mirror of the open access economy itself."
| Finding | Detail |
|---|---|
| Authorship | Mean 3.86 authors/paper · mode 3 (22.9%) · 63.2% of papers have 2–4 authors · range 1–14 |
| Subjects | 67 categories · Computational AI leads (467 papers, 46.0%) · Power engineering 3rd (132) |
| Geography | 91 countries · China (182) and South Korea (170) lead · India 3rd (115) · USA 4th (84) |
| Funding (corrected) | 520 externally funded (51.2%) · 187 institutional/APC-only (18.4%) · 308 no funding (30.3%) |
| Raw vs. corrected | Raw field gives 707 non-null entries (69.7%) — overstates external funding by 187 papers |
| Top funder | NSFC China (30) · NRF Korea (13) · Korean Govt. MSIT (12) · JSPS KAKENHI (10) |