- 1,083 rows = 19 universities × 57 GRAS 2026 subjects; ranks as published by ShanghaiRanking.
- Tsinghua University is in the top 10 of 20 of 57 subjects, the most among the 19; next are Shanghai Jiao Tong University and Zhejiang University, 17 each.
- Across 57 subjects (570 top-10 places, ties aside), mainland China holds 199, the US 219, the UK 48, continental Europe 40; the 16 mainland universities of this dataset hold 110 of China's 199.
- Highly Cited Researchers 2025: 1,423 entries for mainland China and 2,658 for the US; 873 of China's 1,423 are Cross-Field. Counts only, no researcher names.
data.csv. 1,083 rows, 9 columns.
arwu_indicators_by_region.csv. 36 rows, 9 columns.
arwu_tsinghua_indicators.csv. 17 rows, 8 columns.
china_hk_in_top10.csv. 220 rows, 9 columns.
hcr_by_region_category.csv. 84 rows, 6 columns.
hcr_by_university_category.csv. 16 rows, 20 columns.
subject_profile.csv. 19 rows, 19 columns.
top10_by_subject_region.csv. 57 rows, 7 columns.
sources.csv. 59 rows, 6 columns.
data.json: the rows of data.csv as JSON.
schema.json: JSON Schema of data.json.
datapackage.json: Frictionless Data descriptor of every CSV.
Also on Kaggle · Hugging Face
Scope
16 mainland universities in the ARWU 2026 top 100 plus 3 in Hong Kong (HKU and CityUHK are in the top 100, CUHK is kept as a reference).
Codebook
data.csv, 1,083 rows. Unique key: university_id + subject_code.
| Column | Type | Values / unit | What it holds |
|---|---|---|---|
university_id | string | text (19 distinct) | Stable slug of the university, the same id as in the other Long Bet datasets. |
subject_code | string | text (57 distinct) | ShanghaiRanking subject code (e.g. AS0101 Mathematics). |
subject_category | string | text (5 distinct) | GRAS subject category: Natural Sciences, Engineering, Life Sciences, Medical Sciences, Social Sciences. |
subject | string | text (57 distinct) | GRAS subject name. |
gras_2026_rank | string | text (63 distinct) | Rank in the GRAS 2026 subject list as published: a number for places 1-50, a band (e.g. 51-75, 151-200) beyond, or not in published list (top N) when the university is not in a list that is published to place N. |
published_list_length | integer | 50 to 500 | How far down the subject list is published (50, 100, 200, 300, 400 or 500 places). |
source_url | string | text (57 distinct) | Page(s) the figure was read from, "; "-separated. |
source_type | string | official | official (ranking publisher). |
checked_at | string | text (2 distinct) | Date read, ISO 8601. |
Table 1. Columns of data.csv.
china_hk_in_top10.csv, 220 rows. Unique key: subject_code + university_slug.
| Column | Type | Values / unit | What it holds |
|---|---|---|---|
subject_code | string | text (42 distinct) | ShanghaiRanking subject code (e.g. AS0101 Mathematics). |
subject_category | string | text (5 distinct) | GRAS subject category: Natural Sciences, Engineering, Life Sciences, Medical Sciences, Social Sciences. |
subject | string | text (42 distinct) | GRAS subject name. |
gras_2026_rank | integer | 1 to 10 | Rank in the GRAS 2026 subject list as published: a number for places 1-50, a band (e.g. 51-75, 151-200) beyond, or not in published list (top N) when the university is not in a list that is published to place N. |
university_slug | string | text (72 distinct) | ShanghaiRanking institution slug; for the 19 universities of this dataset it equals university_id. |
region | string | text (2 distinct) | Region as printed by ShanghaiRanking (China, China-Hong Kong). |
source_url | string | text (42 distinct) | Page(s) the figure was read from, "; "-separated. |
source_type | string | official | official (ranking publisher). |
checked_at | string | text (1 distinct) | Date read, ISO 8601. |
Table 2. Columns of china_hk_in_top10.csv.
subject_profile.csv, 19 rows. Unique key: university_id.
| Column | Type | Values / unit | What it holds |
|---|---|---|---|
university_id | string | text (19 distinct) | Stable slug of the university, the same id as in the other Long Bet datasets. |
all_top10 | integer | 2 to 20 | Number of the 57 GRAS 2026 subjects in which the university is in places 1-10. |
all_11_50 | integer | 9 to 26 | Number of the 57 subjects with places 11-50. |
all_51_100 | integer | 3 to 17 | Number of the 57 subjects with places 51-100. |
all_beyond_100 | integer | 7 to 20 | Number of the 57 subjects with a published place beyond 100. |
all_not_in_list_top100plus | integer | 0 to 21 | Number of the 57 subjects where the university is not in a list published to 100 places or more. |
all_not_in_list_top50 | integer | 0 to 1 | Number of the 57 subjects where the university is not in a list published only to 50 places (rank unknown beyond 50). |
all_outside_top100 | integer | 7 to 36 | Number of the 57 subjects with no place in the top 100 (all_beyond_100 + all_not_in_list_top100plus). |
stem_top10 | integer | 1 to 19 | Same as all_top10 for the 37 STEM subjects (Natural Sciences, Engineering, Life Sciences). |
stem_11_50 | integer | 6 to 21 | Same as all_11_50 for the 37 STEM subjects. |
stem_51_100 | integer | 1 to 11 | Same as all_51_100 for the 37 STEM subjects. |
stem_beyond_100 | integer | 1 to 14 | Same as all_beyond_100 for the 37 STEM subjects. |
stem_not_in_list_top100plus | integer | 0 to 8 | Same as all_not_in_list_top100plus for the 37 STEM subjects. |
stem_not_in_list_top50 | integer | 0 to 1 | Same as all_not_in_list_top50 for the 37 STEM subjects. |
stem_outside_top100 | integer | 1 to 22 | Same as all_outside_top100 for the 37 STEM subjects. |
source_url | string | text (1 distinct) | Page(s) the figure was read from, "; "-separated. |
source_type | string | official | official (ranking publisher). |
checked_at | string | text (2 distinct) | Date read, ISO 8601. |
note | string | text (1 distinct) | Caveats. |
Table 3. Columns of subject_profile.csv.
top10_by_subject_region.csv, 57 rows. Unique key: subject.
| Column | Type | Values / unit | What it holds |
|---|---|---|---|
subject_category | string | text (5 distinct) | GRAS subject category: Natural Sciences, Engineering, Life Sciences, Medical Sciences, Social Sciences. |
subject | string | text (57 distinct) | GRAS subject name. |
top10_cn | integer | 0 to 10 | Places 1-10 of the subject held by mainland Chinese universities (all of them). |
top10_cn_16 | integer | 0 to 9 | Of top10_cn, places held by the 16 mainland universities of this dataset. |
top10_us | integer | 0 to 10 | Places 1-10 held by US universities. |
top10_uk | integer | 0 to 5 | Places 1-10 held by UK universities. |
top10_europe | integer | 0 to 5 | Places 1-10 held by universities of continental Europe (UK excluded). |
Table 4. Columns of top10_by_subject_region.csv.
arwu_indicators_by_region.csv, 36 rows. Unique key: year + arwu_region.
| Column | Type | Values / unit | What it holds |
|---|---|---|---|
year | integer | 2010 to 2026 | ARWU edition year. |
arwu_region | string | text (6 distinct) | Group of top-100 universities: CN mainland China, HK Hong Kong, US, UK, EU continental Europe (UK excluded), OT all other countries. |
n_in_top100 | integer | 1 to 54 | Number of universities of the group in the ARWU top 100 of that edition. |
alumni | number | 0.0 to 31.2 | ARWU indicator Alumni (alumni winning Nobel Prizes and Fields Medals, weight 10%), score 0-100; mean over the group for the regional table. |
award | number | 0.0 to 41.2 | ARWU indicator Award (staff winning Nobel Prizes and Fields Medals, weight 20%), score 0-100. |
hici | number | 20.6 to 49.4 | ARWU indicator HiCi (highly cited researchers, weight 20%), score 0-100. |
ns | number | 18.7 to 45.7 | ARWU indicator N&S (papers in Nature and Science, weight 20%), score 0-100. |
pub | number | 47.2 to 83.2 | ARWU indicator PUB (papers indexed in SCIE and SSCI, weight 20%), score 0-100. |
pcp | number | 22.9 to 37.9 | ARWU indicator PCP (per capita performance, weight 10%), score 0-100. |
Table 5. Columns of arwu_indicators_by_region.csv.
arwu_tsinghua_indicators.csv, 17 rows. Unique key: year.
| Column | Type | Values / unit | What it holds |
|---|---|---|---|
year | integer | 2010 to 2026 | ARWU edition year. |
rank | string | text (11 distinct) | Tsinghua University ARWU world rank in that edition (band beyond 100). |
alumni | number | 8.7 to 11.9 | ARWU indicator Alumni (alumni winning Nobel Prizes and Fields Medals, weight 10%), score 0-100; mean over the group for the regional table. |
award | integer | 0 to 0 | ARWU indicator Award (staff winning Nobel Prizes and Fields Medals, weight 20%), score 0-100. |
hici | number | 0.0 to 70.9 | ARWU indicator HiCi (highly cited researchers, weight 20%), score 0-100. |
ns | number | 11.8 to 52.6 | ARWU indicator N&S (papers in Nature and Science, weight 20%), score 0-100. |
pub | number | 56.4 to 88.1 | ARWU indicator PUB (papers indexed in SCIE and SSCI, weight 20%), score 0-100. |
pcp | number | 16.1 to 35.4 | ARWU indicator PCP (per capita performance, weight 10%), score 0-100. |
Table 6. Columns of arwu_tsinghua_indicators.csv.
hcr_by_university_category.csv, 16 rows. Unique key: university_id.
| Column | Type | Values / unit | What it holds |
|---|---|---|---|
university_id | string | text (16 distinct) | Stable slug of the university, the same id as in the other Long Bet datasets. |
cross_field | integer | 9 to 54 | Highly Cited Researchers 2025 entries of the university in the category Cross-Field. |
chemistry | integer | 0 to 13 | Highly Cited Researchers 2025 entries of the university in the category Chemistry. |
materials_science | integer | 0 to 10 | Highly Cited Researchers 2025 entries of the university in the category Materials Science. |
engineering | integer | 0 to 12 | Highly Cited Researchers 2025 entries of the university in the category Engineering. |
computer_science | integer | 0 to 4 | Highly Cited Researchers 2025 entries of the university in the category Computer Science. |
physics | integer | 0 to 5 | Highly Cited Researchers 2025 entries of the university in the category Physics. |
mathematics | integer | 0 to 2 | Highly Cited Researchers 2025 entries of the university in the category Mathematics. |
geosciences | integer | 0 to 6 | Highly Cited Researchers 2025 entries of the university in the category Geosciences. |
environment_and_ecology | integer | 0 to 6 | Highly Cited Researchers 2025 entries of the university in the category Environment and Ecology. |
clinical_medicine | integer | 0 to 8 | Highly Cited Researchers 2025 entries of the university in the category Clinical Medicine. |
social_sciences | integer | 0 to 5 | Highly Cited Researchers 2025 entries of the university in the category Social Sciences. |
agricultural_sciences | integer | 0 to 2 | Highly Cited Researchers 2025 entries of the university in the category Agricultural Sciences. |
biology_and_biochemistry | integer | 0 to 2 | Highly Cited Researchers 2025 entries of the university in the category Biology and Biochemistry. |
economics_and_business | integer | 0 to 1 | Highly Cited Researchers 2025 entries of the university in the category Economics and Business. |
immunology | integer | 0 to 7 | Highly Cited Researchers 2025 entries of the university in the category Immunology. |
microbiology | integer | 0 to 4 | Highly Cited Researchers 2025 entries of the university in the category Microbiology. |
molecular_biology_and_genetics | integer | 0 to 1 | Highly Cited Researchers 2025 entries of the university in the category Molecular Biology and Genetics. |
pharmacology_and_toxicology | integer | 0 to 2 | Highly Cited Researchers 2025 entries of the university in the category Pharmacology and Toxicology. |
plant_and_animal_science | integer | 0 to 7 | Highly Cited Researchers 2025 entries of the university in the category Plant and Animal Science. |
Table 7. Columns of hcr_by_university_category.csv.
hcr_by_region_category.csv, 84 rows. Unique key: hcr_region + category.
| Column | Type | Values / unit | What it holds |
|---|---|---|---|
hcr_region | string | text (18 distinct) | Country or region as listed by Clarivate (China Mainland, United States, United Kingdom and 15 European countries). |
category | string | text (23 distinct) | Clarivate Highly Cited Researchers category; ALL = all categories together; Cross-Field = influence across several fields. |
awards | integer | 0 to 2658 | Number of Highly Cited Researchers 2025 entries. 1 entry = researcher x category. |
source_url | string | text (1 distinct) | Page(s) the figure was read from, "; "-separated. |
checked_at | string | text (1 distinct) | Date read, ISO 8601. |
note | string | text (1 distinct) | Caveats. |
Table 8. Columns of hcr_by_region_category.csv.
sources.csv, 59 rows. Unique key: source_id. One row per distinct URL in the columns whose name ends in source_url: source_id, url, title (publisher and host), source_type, accessed_at (latest date a row citing it was checked), used_for (file, column and number of rows).
Method
Data for 13 mainland and 3 Hong Kong universities were collected between 9 and 23 October 2026 from official pages of the universities (admission and international-office pages, fee tables, catalogues), governments, embassies and visa centres, ranking publishers and, for stable facts only, secondary sources; a second verification pass on 23 October 2026 re-read the rows with gaps. An independent re-check of 69 random rows against the source found 64 matching; the 5 differences were corrected or given a lower confidence before release. The three universities that entered the ARWU top 100 in 2026 (Southeast University, Harbin Institute of Technology, Tianjin University) were collected on 8 November 2026 into the same columns; an independent re-check of 20 random rows of that collection found no difference: 8 matching, 6 matching on a secondary or previous-cycle page (marked partial), 6 whose page could not be opened automatically. A second check of 20 other random rows of the three universities, read in a browser on the same day, found 15 matching, 1 matching in part, 1 whose page is no longer online and 3 differences (Southeast University's 2027 admission brochure had replaced the 2025 language and calendar rules; Tianjin University names A-Level results in its scholarship notice); the 3 were corrected before release.
Every row carries source_url (the page the figure was read from) and checked_at (date read).
Text added by the second verification pass starts with "Updated 2026-10-23:". Ids follow the other Long Bet datasets (university_id slugs).
GRAS 2026 places were read from the ShanghaiRanking public ranking API, one call per subject (source_url gives the subject page). Only places of the 19 universities and derived counts are published here; the full subject lists remain the work of ShanghaiRanking and are not copied. A university missing from a subject list gets not in published list (top N), where N is how far ShanghaiRanking publishes that list. ARWU indicator means (arwu_indicators_by_region.csv) average the six indicator scores of the top-100 universities of each group in editions 2010, 2013, 2016, 2019, 2022, 2025 and 2026, read from the ShanghaiRanking API (https://www.shanghairanking.com/api/pub/v1/arwu/rank?version=<year>). Highly Cited Researchers 2025 counts were read from the Clarivate list with its institution, region and category filters, all name variants of each university together and duplicates removed; one entry is one researcher in one category. hcr_by_university_category.csv covers the 16 universities of the first release (13 mainland, 3 in Hong Kong); Southeast University, Harbin Institute of Technology and Tianjin University, added with the ARWU 2026 base, were not counted. No researcher names are published.
Known limitations
- GRAS publishes subject lists to different depths (50 to 500 places), so "not in list" means different things per subject; read
published_list_length. - Rankings count research output; they say little about undergraduate teaching.
- Highly Cited Researchers are assigned to institutions by Clarivate; researchers with several affiliations are counted under the institution filter that lists them. The three universities added with the ARWU 2026 base have no per-university count yet.
- Ranking data are the work of ShanghaiRanking Consultancy and Clarivate; only extracts and derived counts are here.
How to verify
Open the source_url of a row and compare with checked_at. Rankings change once a year. Every URL is listed once in sources.csv.
Cite this
Atamanova, M. (2026). Subject Strength of 19 Chinese Universities: GRAS 2026, ARWU Indicators and Highly Cited Researchers 2025 [Data set]. https://mariascales.com/research/long-bet/data/china-subject-strength/
License
CC BY 4.0 (Creative Commons Attribution 4.0 International, https://creativecommons.org/licenses/by/4.0/) for this compilation: free to share and adapt with attribution, see Cite this.
Ranking data are the work of ShanghaiRanking Consultancy (ARWU, GRAS); researcher counts derive from Clarivate Highly Cited Researchers 2025.