Performance marketing for AI-era growth – backed by models, not gut feeling.
TL;DR
  • 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.
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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.

ColumnTypeValues / unitWhat it holds
university_idstringtext (19 distinct)Stable slug of the university, the same id as in the other Long Bet datasets.
subject_codestringtext (57 distinct)ShanghaiRanking subject code (e.g. AS0101 Mathematics).
subject_categorystringtext (5 distinct)GRAS subject category: Natural Sciences, Engineering, Life Sciences, Medical Sciences, Social Sciences.
subjectstringtext (57 distinct)GRAS subject name.
gras_2026_rankstringtext (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_lengthinteger50 to 500How far down the subject list is published (50, 100, 200, 300, 400 or 500 places).
source_urlstringtext (57 distinct)Page(s) the figure was read from, "; "-separated.
source_typestringofficialofficial (ranking publisher).
checked_atstringtext (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.

ColumnTypeValues / unitWhat it holds
subject_codestringtext (42 distinct)ShanghaiRanking subject code (e.g. AS0101 Mathematics).
subject_categorystringtext (5 distinct)GRAS subject category: Natural Sciences, Engineering, Life Sciences, Medical Sciences, Social Sciences.
subjectstringtext (42 distinct)GRAS subject name.
gras_2026_rankinteger1 to 10Rank 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_slugstringtext (72 distinct)ShanghaiRanking institution slug; for the 19 universities of this dataset it equals university_id.
regionstringtext (2 distinct)Region as printed by ShanghaiRanking (China, China-Hong Kong).
source_urlstringtext (42 distinct)Page(s) the figure was read from, "; "-separated.
source_typestringofficialofficial (ranking publisher).
checked_atstringtext (1 distinct)Date read, ISO 8601.

Table 2. Columns of china_hk_in_top10.csv.

subject_profile.csv, 19 rows. Unique key: university_id.

ColumnTypeValues / unitWhat it holds
university_idstringtext (19 distinct)Stable slug of the university, the same id as in the other Long Bet datasets.
all_top10integer2 to 20Number of the 57 GRAS 2026 subjects in which the university is in places 1-10.
all_11_50integer9 to 26Number of the 57 subjects with places 11-50.
all_51_100integer3 to 17Number of the 57 subjects with places 51-100.
all_beyond_100integer7 to 20Number of the 57 subjects with a published place beyond 100.
all_not_in_list_top100plusinteger0 to 21Number of the 57 subjects where the university is not in a list published to 100 places or more.
all_not_in_list_top50integer0 to 1Number of the 57 subjects where the university is not in a list published only to 50 places (rank unknown beyond 50).
all_outside_top100integer7 to 36Number of the 57 subjects with no place in the top 100 (all_beyond_100 + all_not_in_list_top100plus).
stem_top10integer1 to 19Same as all_top10 for the 37 STEM subjects (Natural Sciences, Engineering, Life Sciences).
stem_11_50integer6 to 21Same as all_11_50 for the 37 STEM subjects.
stem_51_100integer1 to 11Same as all_51_100 for the 37 STEM subjects.
stem_beyond_100integer1 to 14Same as all_beyond_100 for the 37 STEM subjects.
stem_not_in_list_top100plusinteger0 to 8Same as all_not_in_list_top100plus for the 37 STEM subjects.
stem_not_in_list_top50integer0 to 1Same as all_not_in_list_top50 for the 37 STEM subjects.
stem_outside_top100integer1 to 22Same as all_outside_top100 for the 37 STEM subjects.
source_urlstringtext (1 distinct)Page(s) the figure was read from, "; "-separated.
source_typestringofficialofficial (ranking publisher).
checked_atstringtext (2 distinct)Date read, ISO 8601.
notestringtext (1 distinct)Caveats.

Table 3. Columns of subject_profile.csv.

top10_by_subject_region.csv, 57 rows. Unique key: subject.

ColumnTypeValues / unitWhat it holds
subject_categorystringtext (5 distinct)GRAS subject category: Natural Sciences, Engineering, Life Sciences, Medical Sciences, Social Sciences.
subjectstringtext (57 distinct)GRAS subject name.
top10_cninteger0 to 10Places 1-10 of the subject held by mainland Chinese universities (all of them).
top10_cn_16integer0 to 9Of top10_cn, places held by the 16 mainland universities of this dataset.
top10_usinteger0 to 10Places 1-10 held by US universities.
top10_ukinteger0 to 5Places 1-10 held by UK universities.
top10_europeinteger0 to 5Places 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.

ColumnTypeValues / unitWhat it holds
yearinteger2010 to 2026ARWU edition year.
arwu_regionstringtext (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_top100integer1 to 54Number of universities of the group in the ARWU top 100 of that edition.
alumninumber0.0 to 31.2ARWU indicator Alumni (alumni winning Nobel Prizes and Fields Medals, weight 10%), score 0-100; mean over the group for the regional table.
awardnumber0.0 to 41.2ARWU indicator Award (staff winning Nobel Prizes and Fields Medals, weight 20%), score 0-100.
hicinumber20.6 to 49.4ARWU indicator HiCi (highly cited researchers, weight 20%), score 0-100.
nsnumber18.7 to 45.7ARWU indicator N&S (papers in Nature and Science, weight 20%), score 0-100.
pubnumber47.2 to 83.2ARWU indicator PUB (papers indexed in SCIE and SSCI, weight 20%), score 0-100.
pcpnumber22.9 to 37.9ARWU 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.

ColumnTypeValues / unitWhat it holds
yearinteger2010 to 2026ARWU edition year.
rankstringtext (11 distinct)Tsinghua University ARWU world rank in that edition (band beyond 100).
alumninumber8.7 to 11.9ARWU indicator Alumni (alumni winning Nobel Prizes and Fields Medals, weight 10%), score 0-100; mean over the group for the regional table.
awardinteger0 to 0ARWU indicator Award (staff winning Nobel Prizes and Fields Medals, weight 20%), score 0-100.
hicinumber0.0 to 70.9ARWU indicator HiCi (highly cited researchers, weight 20%), score 0-100.
nsnumber11.8 to 52.6ARWU indicator N&S (papers in Nature and Science, weight 20%), score 0-100.
pubnumber56.4 to 88.1ARWU indicator PUB (papers indexed in SCIE and SSCI, weight 20%), score 0-100.
pcpnumber16.1 to 35.4ARWU 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.

ColumnTypeValues / unitWhat it holds
university_idstringtext (16 distinct)Stable slug of the university, the same id as in the other Long Bet datasets.
cross_fieldinteger9 to 54Highly Cited Researchers 2025 entries of the university in the category Cross-Field.
chemistryinteger0 to 13Highly Cited Researchers 2025 entries of the university in the category Chemistry.
materials_scienceinteger0 to 10Highly Cited Researchers 2025 entries of the university in the category Materials Science.
engineeringinteger0 to 12Highly Cited Researchers 2025 entries of the university in the category Engineering.
computer_scienceinteger0 to 4Highly Cited Researchers 2025 entries of the university in the category Computer Science.
physicsinteger0 to 5Highly Cited Researchers 2025 entries of the university in the category Physics.
mathematicsinteger0 to 2Highly Cited Researchers 2025 entries of the university in the category Mathematics.
geosciencesinteger0 to 6Highly Cited Researchers 2025 entries of the university in the category Geosciences.
environment_and_ecologyinteger0 to 6Highly Cited Researchers 2025 entries of the university in the category Environment and Ecology.
clinical_medicineinteger0 to 8Highly Cited Researchers 2025 entries of the university in the category Clinical Medicine.
social_sciencesinteger0 to 5Highly Cited Researchers 2025 entries of the university in the category Social Sciences.
agricultural_sciencesinteger0 to 2Highly Cited Researchers 2025 entries of the university in the category Agricultural Sciences.
biology_and_biochemistryinteger0 to 2Highly Cited Researchers 2025 entries of the university in the category Biology and Biochemistry.
economics_and_businessinteger0 to 1Highly Cited Researchers 2025 entries of the university in the category Economics and Business.
immunologyinteger0 to 7Highly Cited Researchers 2025 entries of the university in the category Immunology.
microbiologyinteger0 to 4Highly Cited Researchers 2025 entries of the university in the category Microbiology.
molecular_biology_and_geneticsinteger0 to 1Highly Cited Researchers 2025 entries of the university in the category Molecular Biology and Genetics.
pharmacology_and_toxicologyinteger0 to 2Highly Cited Researchers 2025 entries of the university in the category Pharmacology and Toxicology.
plant_and_animal_scienceinteger0 to 7Highly 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.

ColumnTypeValues / unitWhat it holds
hcr_regionstringtext (18 distinct)Country or region as listed by Clarivate (China Mainland, United States, United Kingdom and 15 European countries).
categorystringtext (23 distinct)Clarivate Highly Cited Researchers category; ALL = all categories together; Cross-Field = influence across several fields.
awardsinteger0 to 2658Number of Highly Cited Researchers 2025 entries. 1 entry = researcher x category.
source_urlstringtext (1 distinct)Page(s) the figure was read from, "; "-separated.
checked_atstringtext (1 distinct)Date read, ISO 8601.
notestringtext (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

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.

Where these numbers are used
China: The Second Look (Article 7) The article whose tables are built from this dataset.
Studying in China: Tuition, Living Costs, Year Totals and Scholarships (2026) Tuition by track, published living-cost items, an estimated full year and the scholarships and government quotas open to international bachelor students.
Bachelor Programmes Open to International Students at Chinese Universities (2026) Every bachelor programme in the international catalogues of the universities, with language of instruction and a STEM flag, plus counts per university.
China and Hong Kong Entry Rules for International Bachelor Applicants (2026) Which school certificates are accepted, admission calendars, language and preparatory routes, the HSK 3.0 transition, apostille, study visas by passport and work rules for students.

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.

About the author

Maria Atamanova is a Senior Growth & Performance Marketing Manager and applied mathematician. Twelve years of growth in apps, e-commerce, EdTech and AI data platforms.

The method here – define the criteria, build the dataset, test every obvious answer – is the same one I use for growth teams; see the case studies.