# Datasets, Page 59 of 132 Source: https://savrn.com/datasets/page/59 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub # AI Training Datasets Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it. 11,122Models 3,164Datasets 285Papers 6,792Publishers 11,852Sourced relationships Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Datasets Task - [Text generation127](https://savrn.com/datasets?task=Text+generation) - [Robotics106](https://savrn.com/datasets?task=Robotics) - [Other48](https://savrn.com/datasets?task=Other) - [Text classification47](https://savrn.com/datasets?task=Text+classification) - [Text retrieval44](https://savrn.com/datasets?task=Text+retrieval) - [Question answering36](https://savrn.com/datasets?task=Question+answering) - [Speech recognition33](https://savrn.com/datasets?task=Speech+recognition) - [Tabular classification21](https://savrn.com/datasets?task=Tabular+classification) - [Time series forecasting21](https://savrn.com/datasets?task=Time+series+forecasting) - [Image to text17](https://savrn.com/datasets?task=Image+to+text) - [Image classification16](https://savrn.com/datasets?task=Image+classification) - [Object detection14](https://savrn.com/datasets?task=Object+detection) - [Text to speech14](https://savrn.com/datasets?task=Text+to+speech) - [Visual question answering14](https://savrn.com/datasets?task=Visual+question+answering) - [Image to image11](https://savrn.com/datasets?task=Image+to+image) - [Image segmentation9](https://savrn.com/datasets?task=Image+segmentation) - [Image to 3d9](https://savrn.com/datasets?task=Image+to+3d) - [Translation9](https://savrn.com/datasets?task=Translation) - [Video classification9](https://savrn.com/datasets?task=Video+classification) - [Audio classification7](https://savrn.com/datasets?task=Audio+classification) - [Feature extraction7](https://savrn.com/datasets?task=Feature+extraction) - [Reinforcement learning7](https://savrn.com/datasets?task=Reinforcement+learning) - [Text to image7](https://savrn.com/datasets?task=Text+to+image) - [Text to video7](https://savrn.com/datasets?task=Text+to+video) License - [other250](https://savrn.com/datasets?license=other) - [apache-2.0236](https://savrn.com/datasets?license=apache-2.0) - [mit232](https://savrn.com/datasets?license=mit) - [cc-by-4.0224](https://savrn.com/datasets?license=cc-by-4.0) - [cc-by-nc-4.077](https://savrn.com/datasets?license=cc-by-nc-4.0) - [cc0-1.030](https://savrn.com/datasets?license=cc0-1.0) - [cc-by-sa-4.029](https://savrn.com/datasets?license=cc-by-sa-4.0) - [odc-by24](https://savrn.com/datasets?license=odc-by) - [cc-by-nc-sa-4.020](https://savrn.com/datasets?license=cc-by-nc-sa-4.0) - [unknown14](https://savrn.com/datasets?license=unknown) - [cc-by-sa-3.06](https://savrn.com/datasets?license=cc-by-sa-3.0) - [odbl6](https://savrn.com/datasets?license=odbl) - [gpl-3.05](https://savrn.com/datasets?license=gpl-3.0) - [cc-by-3.04](https://savrn.com/datasets?license=cc-by-3.0) - [cc3](https://savrn.com/datasets?license=cc) - [cc-by-nc-nd-4.03](https://savrn.com/datasets?license=cc-by-nc-nd-4.0) - [gpl3](https://savrn.com/datasets?license=gpl) - [cc-by-2.02](https://savrn.com/datasets?license=cc-by-2.0) - [cc-by-nd-4.02](https://savrn.com/datasets?license=cc-by-nd-4.0) - [cdla-sharing-1.02](https://savrn.com/datasets?license=cdla-sharing-1.0) - [agpl-3.01](https://savrn.com/datasets?license=agpl-3.0) - [bsl-1.01](https://savrn.com/datasets?license=bsl-1.0) - [c-uda1](https://savrn.com/datasets?license=c-uda) - [cc-by-nc-3.01](https://savrn.com/datasets?license=cc-by-nc-3.0) Access - [Publicly accessible2,933](https://savrn.com/datasets?access=open_access) - [Access requested at publisher231](https://savrn.com/datasets?access=gated) Publisher - [Benjamin J Barber35](https://savrn.com/datasets?publisher=endomorphosis) - [Chang Yu24](https://savrn.com/datasets?publisher=g1n0st) - [Ji Xie23](https://savrn.com/datasets?publisher=sanaka87) - [Justicedao18](https://savrn.com/datasets?publisher=justicedao) - [LeDuy13](https://savrn.com/datasets?publisher=duyle2408) - [Nguyen13](https://savrn.com/datasets?publisher=icdn11) - [Nguyen13](https://savrn.com/datasets?publisher=icdn12) - [Nguyen13](https://savrn.com/datasets?publisher=icdn13) - [National Engineering Research Center for Big Data Software13](https://savrn.com/datasets?publisher=thulab) - [Nguyen12](https://savrn.com/datasets?publisher=icdn10) - [Nguyen12](https://savrn.com/datasets?publisher=icdn14) - [Byte I12](https://savrn.com/datasets?publisher=icdn15) - [Rebrowser.net12](https://savrn.com/datasets?publisher=rebrowser) - [Shufan Jiang12](https://savrn.com/datasets?publisher=tsumugii) - [Ai210](https://savrn.com/datasets?publisher=allenai) - [Ps Yo10](https://savrn.com/datasets?publisher=atokforps) - [Dougall DeepMind10](https://savrn.com/datasets?publisher=dougalldeepmind) - [Deesced9](https://savrn.com/datasets?publisher=ameluce) - [Jiahao Zhang9](https://savrn.com/datasets?publisher=hi-todayis-jh) - [Julia Dollis9](https://savrn.com/datasets?publisher=juliadollis) - [Massive Text Embedding Benchmark9](https://savrn.com/datasets?publisher=mteb) - [Pranab Pattanaik9](https://savrn.com/datasets?publisher=p2samapa) - [Sota9](https://savrn.com/datasets?publisher=sotaagi2030) - [Sunsetcanyon9](https://savrn.com/datasets?publisher=sunsetcanyon) Size - [10K1T16](https://savrn.com/datasets?size=n%3E1T) - [1B1M1](https://savrn.com/datasets?size=n%3E1M) Domain - [LeRobot31](https://savrn.com/datasets?domain=LeRobot) - [legal, law, graphrag12](https://savrn.com/datasets?domain=legal%2C+law%2C+graphrag) - [tsfile, timeseries, time-series12](https://savrn.com/datasets?domain=tsfile%2C+timeseries%2C+time-series) - [open-data, data-science, web-scraping9](https://savrn.com/datasets?domain=open-data%2C+data-science%2C+web-scraping) - [seo-cluster, web-nodes9](https://savrn.com/datasets?domain=seo-cluster%2C+web-nodes) - [dpo, rlhf, ultrafeedback8](https://savrn.com/datasets?domain=dpo%2C+rlhf%2C+ultrafeedback) - [mteb, text, image7](https://savrn.com/datasets?domain=mteb%2C+text%2C+image) - [nepali, instruction-tuning, agriculture7](https://savrn.com/datasets?domain=nepali%2C+instruction-tuning%2C+agriculture) - [science, stackexchange, provenance5](https://savrn.com/datasets?domain=science%2C+stackexchange%2C+provenance) - [LeRobot, robotics, lerobot4](https://savrn.com/datasets?domain=LeRobot%2C+robotics%2C+lerobot) - [ml-intern4](https://savrn.com/datasets?domain=ml-intern) - [AMASS, Retarget, Robotics3](https://savrn.com/datasets?domain=AMASS%2C+Retarget%2C+Robotics) - [benchmark, agents, terminal3](https://savrn.com/datasets?domain=benchmark%2C+agents%2C+terminal) - [climate3](https://savrn.com/datasets?domain=climate) - [code3](https://savrn.com/datasets?domain=code) - [crypto, cryptocurrency, finance3](https://savrn.com/datasets?domain=crypto%2C+cryptocurrency%2C+finance) - [data-attribution, influence-functions, interpretability3](https://savrn.com/datasets?domain=data-attribution%2C+influence-functions%2C+interpretability) - [fireviewer, fire, smoke3](https://savrn.com/datasets?domain=fireviewer%2C+fire%2C+smoke) - [video3](https://savrn.com/datasets?domain=video) - [LeRobot, lerobot, grabette2](https://savrn.com/datasets?domain=LeRobot%2C+lerobot%2C+grabette) - [LeRobot, ur7e, robotiq2](https://savrn.com/datasets?domain=LeRobot%2C+ur7e%2C+robotiq) - [agentenv, rl-environment, agents2](https://savrn.com/datasets?domain=agentenv%2C+rl-environment%2C+agents) - [ai-governance, measurement2](https://savrn.com/datasets?domain=ai-governance%2C+measurement) - [autonomous, agents, evolution2](https://savrn.com/datasets?domain=autonomous%2C+agents%2C+evolution) [Most downloaded](https://savrn.com/datasets?sort=downloads)[Most liked](https://savrn.com/datasets?sort=likes)[Recently updated](https://savrn.com/datasets?sort=updated)[Name](https://savrn.com/datasets?sort=name) 3,164 datasets, sorted by most downloaded. Dataset ### [express](https://savrn.com/datasets/express-3) [Joan Paul Ellis](https://savrn.com/model-publishers/joanellis38682) Publicly accessible [View dataset](https://savrn.com/datasets/express-3) Dataset ### [cinnamon](https://savrn.com/datasets/cinnamon) [Pham Ngoc Trang](https://savrn.com/model-publishers/trangpham91842) Publicly accessible [View dataset](https://savrn.com/datasets/cinnamon) Dataset ### [Greenland-GPS-PWV](https://savrn.com/datasets/greenland-gps-pwv) [Scallions_](https://savrn.com/model-publishers/scallions) 最后更新 (last updated): 2026-10-17 11:32 CST (UTC+8) 试点年份 (pilot year) 2020；区域 (region) 55–84°N, 80–10°W 与 ERA5 TCWV、IGRA2 的对比结果在 12 个月处理完后生成于 processed/comparison/（目前为基于 NGL 自带 PWV 的临时对比 provisional）。 Hourly GNSS precipitable water vapour (PWV) for ~119 GNSS stations in Greenland, Iceland and the Canadian Arctic (55–84°N, 80–10°W), derived from Nevada Geodetic Laboratory 5-min zenith total delays, with ERA5-based ZHD (Saastamoinen) and Tm, and comparisons against ERA5 TCWV and IGRA2 radiosonde PW. See METHODS.md. - raw/ngl/ NGL IGS20 tropo yearly zips (+ station list files), raw/era5/ monthly ERA5 NetCDF (single + pressure levels), raw/igra2/ IGRA2 data-por / derived-por zips. Full manifest… Publicly accessible cc-by-4.0 [View dataset](https://savrn.com/datasets/greenland-gps-pwv) Dataset ### [Virtual4DSG-part2](https://savrn.com/datasets/virtual4dsg-part2) [Zzae](https://savrn.com/model-publishers/jaewonbae) Publicly accessible [View dataset](https://savrn.com/datasets/virtual4dsg-part2) Dataset ### [fundos-cvm](https://savrn.com/datasets/fundos-cvm) [Lucas](https://savrn.com/model-publishers/shote) Publicly accessible [View dataset](https://savrn.com/datasets/fundos-cvm) Dataset · Image segmentation ### [TreeUQ](https://savrn.com/datasets/treeuq) [ICLR KIWI](https://savrn.com/model-publishers/iclr2027kiwi) TreeUQ is a large-scale Earth observation benchmark for tree species mapping and tree structure estimation (height, count, density, variance) over Bavaria, Germany. Each 128×128 pixel patch (10 m resolution, EPSG:25832) contains: - Sentinel-2 — 4 seasonal composites (spring/summer/autumn/winter 2025), 10 bands - Sentinel-1 GRD — 4 seasonal composites, VV + VH polarisation, linear gamma-0 - Tree species raster — Bavarian species classification aligned to the 10 m grid - Supervision labels — per-pixel height, count, density from the Bavarian Einzelbäume individual-tree inventory - DOP20 aerial imagery (optional) — 20 cm RGB orthophotos as 6400×6400 chips Splits (spatial blocks): train 31,806… Publicly accessible cc-by-4.0 1T