Top AI research institutions (weekly)
Weekly ranking of the institutions driving AI research, from the official keyless OpenAlex API (https://api.openalex.org, CC0 data). Trailing 30 complete days: works tagged with the Artificial intelligence concept (C154945302) are grouped by institution (top 200), and a second all-works group_by over the same window (batched, with a single-count fallback) supplies each institution's total output so an AI share of its own production can be computed. Institution identity is resolved on OpenAlex's canonical ids (ROR-linked), so one university never appears twice; countries are normalized to ISO alpha-3 via hub.normalize; the OpenAlex institution type is carried as a coarse classification. The impact_score is a documented 0-100 composite = 50% min-max-normalized AI works per day + 50% min-max-normalized AI share of trailing-30-day output, and institution_rank orders by impact_score descending (ties: AI works desc, then OpenAlex id). as-of stamping is day-granular (window end), so re-running inside the same window is a no-op. Columns: ISO week of the window end, as-of date, window start/end, OpenAlex id / institution URL / name / ROR, ISO alpha-3 country code and name, institution type, AI works and total works in the window, AI share (%), AI works per day, impact score (0-100), institution rank. Primary key: (week, openalex_id). Cadence: weekly. Nullability: country_code, ror, homepage_url may be empty when OpenAlex has none; ai_works, total_works, ai_share_pct, impact_score and institution_rank are never null. Caveats: OpenAlex concept tagging is automated — bulk mis-tagging can inflate a few rows (see the facility-type rows near the top); counts are full (not fractional) attributions, so co-authored works credit every institution; ai_share is clipped at 100% if the API's two sweeps ever disagree. Sample use: order by institution_rank for the week's leading AI research producers, or filter country_code = 'CHN'.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 200
- Colonnes
- 18
- Cadence de la source
- Hebdomadaire
- Dernière actualisation
- 25 sept. 2026
- Thème
- technology
| Colonne | Type | Description |
|---|---|---|
| week | string | ISO week of the window end (e.g. 2026-W39); primary-key component. (unit: ISO week) |
| as_of | string | Window-end date; day-granular as-of stamp so re-runs inside the window are idempotent. (unit: date) |
| window_start | string | First publication date included in the trailing 30-day window. (unit: date) |
| window_end | string | Last publication date included (yesterday, UTC). (unit: date) |
| openalex_id | string | Canonical OpenAlex institution id (e.g. I1294671590); entity-resolution key, ROR-linked. (unit: id) |
| institution_url | string | Canonical OpenAlex institution page URL; never null. (unit: url) |
| institution_name | string | OpenAlex canonical display name; stripped; never null. (unit: text) |
| institution_ror | string | ROR id URL when OpenAlex knows one; empty otherwise. (unit: url) |
| homepage_url | string | Institution homepage URL when OpenAlex knows one; empty otherwise. (unit: url) |
| country_code | string | ISO 3166-1 alpha-3 country code normalized from OpenAlex's alpha-2 via hub.normalize; empty when unknown. (unit: ISO alpha-3) |
| country_name | string | Canonical English country name for country_code; empty when unknown. (unit: text) |
| institution_type | string | OpenAlex coarse institution type: education, company, government, nonprofit, facility, healthcare, archive, or other. (unit: category) |
| ai_works | integer | Works published in the window tagged with the Artificial intelligence concept; full (not fractional) attribution. (unit: count) |
| total_works | integer | All works published in the window by the institution; denominator for ai_share_pct. (unit: count) |
| ai_share_pct | float | 100 * ai_works / total_works, clipped at 100%; the institution's AI focus. (unit: percent) |
| ai_works_per_day | float | ai_works / 30 (fixed window length). (unit: works/day) |
| impact_score | float | Output-impact composite: 50% min-max-normalized ai_works_per_day + 50% min-max-normalized ai_share_pct, scaled 0-100 within the snapshot. (unit: 0-100) |
| institution_rank | integer | Rank by impact_score descending (1 = highest); ties broken by ai_works desc, then openalex_id. (unit: rank) |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| week | as_of | window_start | window_end | openalex_id | institution_url | institution_name | institution_ror | homepage_url | country_code | country_name | institution_type | ai_works | total_works | ai_share_pct | ai_works_per_day | impact_score | institution_rank |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I7935750 | https://openalex.org/I7935750 | Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures | https://ror.org/02tyer376 | https://www.dsmz.de/ | DEU | Germany | facility | 2 116 | 2 158 | 98,05 | 70,53 | 100 | 1 |
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I153151563 | https://openalex.org/I153151563 | Harrisburg University of Science and Technology | https://ror.org/02g0s4z48 | https://www.harrisburgu.edu | USA | United States | education | 160 | 177 | 90,4 | 5,33 | 48,21 | 2 |
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I4210104407 | https://openalex.org/I4210104407 | Open Society | https://ror.org/01gp9yw74 | http://www.otevrenaspolecnost.cz/en | CZE | Czechia | nonprofit | 94 | 154 | 61,04 | 3,13 | 31,57 | 3 |
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I4210100255 | https://openalex.org/I4210100255 | Beijing Academy of Artificial Intelligence | https://ror.org/016a74861 | https://www.baai.ac.cn/ | CHN | China | other | 112 | 196 | 57,14 | 3,73 | 30,01 | 4 |
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I4210164862 | https://openalex.org/I4210164862 | Artificial Intelligence in Medicine (Canada) | https://ror.org/05p590m36 | http://www.aim.ca/ | CAN | Canada | company | 108 | 214 | 50,47 | 3,6 | 26,5 | 5 |
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I139759216 | https://openalex.org/I139759216 | Beijing University of Posts and Telecommunications | https://ror.org/04w9fbh59 | https://www.bupt.edu.cn | CHN | China | education | 93 | 191 | 48,69 | 3,1 | 25,22 | 6 |
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I1291425158 | https://openalex.org/I1291425158 | Google (United States) | https://ror.org/00njsd438 | https://www.google.com/ | USA | United States | company | 95 | 213 | 44,6 | 3,17 | 23,18 | 7 |
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I149594827 | https://openalex.org/I149594827 | Xidian University | https://ror.org/05s92vm98 | https://www.xidian.edu.cn | CHN | China | education | 139 | 328 | 42,38 | 4,63 | 23,12 | 8 |
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I875944469 | https://openalex.org/I875944469 | Koneru Lakshmaiah Education Foundation | https://ror.org/02k949197 | http://www.kluniversity.in/ | IND | India | education | 95 | 237 | 40,08 | 3,17 | 20,87 | 9 |
| 2026-W39 | 2026-09-24 | 2026-08-26 | 2026-09-24 | I887064364 | https://openalex.org/I887064364 | University of Amsterdam | https://ror.org/04dkp9463 | https://www.uva.nl | NLD | Netherlands | education | 441 | 1 910 | 23,09 | 14,7 | 20,63 | 10 |
Profilé le 25 sept. 2026 à partir de l’instantané 20260925T063105Z-5b09e2ffa2fd
Mesuré- Complétude
- 100 %
- Lignes
- 200
- Colonnes
- 18
- Colonnes incomplètes
- 0
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| weekvarchar | 0 % | 1 | — |
|
| as_ofvarchar | 0 % | 1 | — |
|
| window_startvarchar | 0 % | 1 | — |
|
| window_endvarchar | 0 % | 1 | — |
|
| openalex_idvarchar | 0 % | 165 | — |
|
| institution_urlvarchar | 0 % | 203 | — |
|
| institution_namevarchar | 0 % | 225 | — |
|
| institution_rorvarchar | 0 % | 177 | — |
|
| homepage_urlvarchar | 0 % | 199 | — |
|
| country_codevarchar | 0 % | 25 | — |
|
| country_namevarchar | 0 % | 36 | — |
|
| institution_typevarchar | 0 % | 6 | — |
|
| ai_worksbigint | 0 % | 115 | 73 → 2 116médiane 106 | 2 hors du 1er–99e centile |
| total_worksbigint | 0 % | 239 | 154 → 19 419médiane 846,5 | 4 hors du 1er–99e centile |
| ai_share_pctdouble | 0 % | 158 | 0,38 → 98,05médiane 12,74 | 4 hors du 1er–99e centile |
| ai_works_per_daydouble | 0 % | 96 | 2,43 → 70,53médiane 3,53 | 2 hors du 1er–99e centile |
| impact_scoredouble | 0 % | 199 | 0,03 → 100médiane 7,54 | 4 hors du 1er–99e centile |
| institution_rankbigint | 0 % | 223 | 1 → 200médiane 100,5 | 4 hors du 1er–99e centile |
- Actuelle
20260925T063105Z-5b09e2ffa2fd · sha256 5b09e2ffa2fd…
200 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/openalex_ai_institutions/openalex_ai_institutions_weekly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/openalex_ai_institutions/openalex_ai_institutions_weekly").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API : https://datazimuts.com/v1/datasets/openalex_ai_institutions/openalex_ai_institutions_weekly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20260925T063105Z-5b09e2ffa2fd et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
OpenAlex AI research institutions (agent-curated). (2026). Top AI research institutions (weekly) [Data set, snapshot 20260925T063105Z-5b09e2ffa2fd, sha256 5b09e2ffa2fd]. Datazimuts. Retrieved 2026-09-25, from https://datazimuts.com/fr/datasets/openalex_ai_institutions/openalex_ai_institutions_weekly?snapshot=20260925T063105Z-5b09e2ffa2fd
@misc{dz_openalex_ai_institutions_openalex_ai_ins_5b09e2ff,
title = {{Top AI research institutions (weekly)}},
author = {{OpenAlex AI research institutions (agent-curated)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/openalex_ai_institutions/openalex_ai_institutions_weekly?snapshot=20260925T063105Z-5b09e2ffa2fd}},
note = {Snapshot 20260925T063105Z-5b09e2ffa2fd, sha256 5b09e2ffa2fd49c8a9ea5fac14c46cc2b088fc2ae1736e2e16545fabb535eefd; accessed 2026-09-25}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=openalex_ai_institutions%2Fopenalex_ai_institutions_weekly&lang=fr&theme=auto&snapshot=20260925T063105Z-5b09e2ffa2fd&x=week&y=ai_works&agg=avg" title="Top AI research institutions (weekly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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