# Datasets, Page 64 of 132
Source: https://savrn.com/datasets/page/64
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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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

- [10K<n<100K125](https://savrn.com/datasets?size=10K%3Cn%3C100K)
- [1K<n<10K106](https://savrn.com/datasets?size=1K%3Cn%3C10K)
- [n<1K100](https://savrn.com/datasets?size=n%3C1K)
- [100K<n<1M70](https://savrn.com/datasets?size=100K%3Cn%3C1M)
- [1M<n<10M51](https://savrn.com/datasets?size=1M%3Cn%3C10M)
- [10M<n<100M33](https://savrn.com/datasets?size=10M%3Cn%3C100M)
- [100M<n<1B21](https://savrn.com/datasets?size=100M%3Cn%3C1B)
- [n>1T16](https://savrn.com/datasets?size=n%3E1T)
- [1B<n<10B8](https://savrn.com/datasets?size=1B%3Cn%3C10B)
- [100B<n<1T5](https://savrn.com/datasets?size=100B%3Cn%3C1T)
- [10B<n<100B2](https://savrn.com/datasets?size=10B%3Cn%3C100B)
- [10GB<n<100GB1](https://savrn.com/datasets?size=10GB%3Cn%3C100GB)
- [120,8981](https://savrn.com/datasets?size=120%2C898)
- [1B-tokens1](https://savrn.com/datasets?size=1B-tokens)
- [1T<n<10T1](https://savrn.com/datasets?size=1T%3Cn%3C10T)
- [n1K<n10K1](https://savrn.com/datasets?size=n1K%3Cn10K)
- [n<100K1](https://savrn.com/datasets?size=n%3C100K)
- [n<10K1](https://savrn.com/datasets?size=n%3C10K)
- [n<1GB1](https://savrn.com/datasets?size=n%3C1GB)
- [n>1M1](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 · Text retrieval

### [ipfs_northmacedonia_laws](https://savrn.com/datasets/ipfs-northmacedonia-laws)

[Benjamin J Barber](https://savrn.com/model-publishers/endomorphosis)

Research snapshot of official national legislation from Sluzben vesnik (slvesnik.com.mk) — live FreeIssue API + Issues PDFs; Wayback of official URLs. Not legal advice. The official gazette / authentic source prevails over this corpus. - data/laws.parquet — one row per instrument. - data/articles.parquet — article/section rows for the same snapshot. - scrapers/collectmk.py — research collector used to build this snapshot. Official Gazette of the Republic of North Macedonia (Sluzben vesnik, slvesnik.com.mk). Authentic gazette PDF prevails. Not legal advice.. This dataset is not legal advice. The official source prevails. - https://slvesnik.com.mk/ Resume 2026-09-25 (PT) batches G/H: G ok=250…

Publicly accessible other 100K<n<1M

[View dataset](https://savrn.com/datasets/ipfs-northmacedonia-laws)

Dataset

### [antares](https://savrn.com/datasets/antares)

[Pham Minh Trang](https://savrn.com/model-publishers/trangpham40166)

Publicly accessible

[View dataset](https://savrn.com/datasets/antares)

Dataset

### [guardrail-lora-qlora-finetune](https://savrn.com/datasets/guardrail-lora-qlora-finetune)

[Ahmed Alsebaei](https://savrn.com/model-publishers/ahmed21345)

Access requested at publisher

[View dataset](https://savrn.com/datasets/guardrail-lora-qlora-finetune)

Dataset

### [compass](https://savrn.com/datasets/compass-2)

[Vu Hai Khoa](https://savrn.com/model-publishers/khoavu57095)

Publicly accessible

[View dataset](https://savrn.com/datasets/compass-2)

Dataset

### [data-road_network_eta](https://savrn.com/datasets/data-road-network-eta)

[CAMUS Lab](https://savrn.com/model-publishers/camus-lab)

ETA(예상소요시간) 사용 가능합니다. 각 region 마다 self-contained 파일 두 개: - {region}osmv3nodes.parquet — 노드 (nodeid, lat, lon) - {region}osmv3edges.parquet — 엣지 (source, target, lengthm, highway, oneway, ktdbmatched, …, 시간대별 속도 컬럼) seoul, busan, daegu, incheon, gwangju, daejeon, ulsan, sejong, gyeonggi, gangwon, chungbuk, chungnam, jeonbuk, jeonnam, gyeongbuk, gyeongnam 스키마: {daytype}{slot}p50 (단위: km/h) - daytype ∈ {weekday, weekend} - slot ∈ - observed (KTDB 직접 관측): ampeak, midday, pmpeak, offpeak - derived (관측 4슬롯에서 도출): amshoulder, afternoon, pmshoulder, night 프로젝트 코드(phasedattach.py) 참조. scipy 또는 networkx 등 일반 라우팅 라이브러리에 그대로 투입 가능합니다. phasec (위계 클립) → phased (attach + derived slots) 원본 출처(OSM, KTDB)의…

Publicly accessible cc-by-nc-4.0 100K<n<1M

[View dataset](https://savrn.com/datasets/data-road-network-eta)

Dataset

### [pre-training-tokens](https://savrn.com/datasets/pre-training-tokens)

[Johannes Schwab](https://savrn.com/model-publishers/diamegs)

Publicly accessible

[View dataset](https://savrn.com/datasets/pre-training-tokens)

Dataset

### [tiuslaplexip](https://savrn.com/datasets/tiuslaplexip)

[PixelPals_AIC2026](https://savrn.com/model-publishers/pixelpalsuit)

Publicly accessible

[View dataset](https://savrn.com/datasets/tiuslaplexip)

Dataset

### [tamil-asr-segmented](https://savrn.com/datasets/tamil-asr-segmented)

[Kingskingdom](https://savrn.com/model-publishers/king758)

High-quality segmented Tamil speech clips with embedded audio for ASR training.

Access requested at publisher

[View dataset](https://savrn.com/datasets/tamil-asr-segmented)

Dataset

### [humanoid-locoman-vla-runs](https://savrn.com/datasets/humanoid-locoman-vla-runs)

[Lukas Müller](https://savrn.com/model-publishers/lukasmueller)

Access requested at publisher

[View dataset](https://savrn.com/datasets/humanoid-locoman-vla-runs)

Dataset

### [abl-ppocr-train](https://savrn.com/datasets/abl-ppocr-train)

[Aldi Bayu Saputro](https://savrn.com/model-publishers/jembyutkuda-liarnyoynyoy)

Publicly accessible

[View dataset](https://savrn.com/datasets/abl-ppocr-train)

Dataset

### [monsoon](https://savrn.com/datasets/monsoon)

[Richard Paige Parker](https://savrn.com/model-publishers/richardpparker5110)

Publicly accessible

[View dataset](https://savrn.com/datasets/monsoon)

Dataset

### [voxpopuli_da_precomputed_17b](https://savrn.com/datasets/voxpopuli-da-precomputed-17b)

[Daniel Biørrith](https://savrn.com/model-publishers/biorrith)

Publicly accessible

[View dataset](https://savrn.com/datasets/voxpopuli-da-precomputed-17b)

Dataset · Tabular to text

### [weather-rescue-brazil](https://savrn.com/datasets/weather-rescue-brazil)

[Gabriel Bueno](https://savrn.com/model-publishers/akagabi)

Daily meteorological observations from Brazilian observatories, 1883–1890, transcribed from printed nineteenth-century tables by an open 2B model running offline, with every row carrying its provenance and a quality verdict. readings a day), air temperature (mean, max, min, in-shelter and unsheltered), vapour tension, relative humidity, wind direction and force, cloudiness, rainfall, evaporation, ozone, and for Cuyabá the height of the Rio Cuyabá. Each row is one printed line: valuesasprinted (what the page shows, no silent correction), values (the publication's conventions undone in code, e.g. the barometer's elided leading digit restored), markers (words the page prints instead of a…

Publicly accessible cc0-1.0 1K<n<10K

[View dataset](https://savrn.com/datasets/weather-rescue-brazil)

Dataset · Text generation

### [ai-tech-articles](https://savrn.com/datasets/ai-tech-articles)

[Amittai](https://savrn.com/model-publishers/siavava)

This dataset is a collection of AI/tech articles scraped from the web: the 2023 corpus collected by the original Haskell scraper plus everything the Rust crawler has added since (2023 onwards, with full publication dates). It's hosted on HuggingFace Datasets, so it is easier to load in and work with. You do not need to clone this repo. HuggingFace will download the dataset for you, the first time that you load it, and cache it locally so it does not need to re-download it again (unless it detects a change upstream). - example.ipynb: load the dataset and look at a few rows. - analytics.ipynb: counts by year, month and domain, article lengths, sample titles. This repo is a small Python…

Publicly accessible mit

[View dataset](https://savrn.com/datasets/ai-tech-articles)

Dataset

### [ginger](https://savrn.com/datasets/ginger)

[Le Ngoc Khanh](https://savrn.com/model-publishers/khanhle53794)

Publicly accessible

[View dataset](https://savrn.com/datasets/ginger)

Dataset

### [cumulus](https://savrn.com/datasets/cumulus-2)

[Le Bao An](https://savrn.com/model-publishers/anle16047)

Publicly accessible

[View dataset](https://savrn.com/datasets/cumulus-2)

Dataset

### [Adata_archive](https://savrn.com/datasets/adata-archive)

[Kush](https://savrn.com/model-publishers/lih-kin)

Access requested at publisher

[View dataset](https://savrn.com/datasets/adata-archive)

Dataset

### [b635](https://savrn.com/datasets/b635)

[Yihe Zang](https://savrn.com/model-publishers/assassinlike)

Each baseline directory contains its own data, documentation, and provenance. Gym-Anything also includes environment scripts and task assets. The default configuration is an alias for gym-anything; each configuration uses the test split. Load Benchmark-Agent with loaddataset("assassinlike/b635", "benchmark-agent", split="test"). Benchmark-Agent Qwen and Sol answers and scores: evaluations, configuration benchmark-agent-evaluations (4,290 records). Petri: 160 complete audits (80 DeepSeek Flash targets and 80 Qwen3.8-27B targets), including full trajectories, primary requirement judgments, and all 23 native reference scores. See Petri records; dataset configuration petri.

Publicly accessible 1K<n<10K

[View dataset](https://savrn.com/datasets/b635)

Dataset

### [lorry](https://savrn.com/datasets/lorry)

[Duong Quang Dung](https://savrn.com/model-publishers/dungduong27742)

Publicly accessible

[View dataset](https://savrn.com/datasets/lorry)

Dataset

### [temperature_digital_twin](https://savrn.com/datasets/temperature-digital-twin)

[Simon Sassi](https://savrn.com/model-publishers/maxerbox)

PVVX BLE sensor readings (temperature, humidity, battery) collected via TheengsGateway → MQTT → dlt pipeline.

Publicly accessible

[View dataset](https://savrn.com/datasets/temperature-digital-twin)

Dataset · Speech recognition

### [uzbek-youtube-audio-subs](https://savrn.com/datasets/uzbek-youtube-audio-subs)

[Muzaffar](https://savrn.com/model-publishers/ismatillayevmuzaffar)

Audio from Uzbek-language YouTube podcasts / audiobooks / fairy tales, in original quality (no re-encoding), together with YouTube subtitles: manual tracks and original-language auto-captions with word-level timings. audio/ / /.opus|.m4a subtitles/ / /.json — {"tracks":[{"lang","kind":"manual|auto","segments":[{"start","end","text","words":[{"start","end","w"}]}]}]} metadata/part-.jsonl — one row per video (title, channel, duration, paths, subtitle tracks) Rights to the content belong to the original authors/channels; intended for research use.

Publicly accessible other

[View dataset](https://savrn.com/datasets/uzbek-youtube-audio-subs)

Dataset · Text classification

### [Game-Intention](https://savrn.com/datasets/game-intention)

[Game Intention](https://savrn.com/model-publishers/gameintention)

A cross-game intent-forecasting benchmark: from tournament commentary (+ video frames), predict the next-stage gameplay intention of professional players, across repo's root corresponds to the local data/ directory. To use with the code, clone this repo into your code checkout as data/ (or symlink it there): - 31 / 47 matches annotated (intents/.intent.json); 4 games complete at 5 (dota, lol, pubg, wc3). - 16 matches pending ASR+intent — their VODs are already in raw/; pipeline rerun on a GPU machine. - See DATASETSTATUS.md for the per-game done/missing breakdown + migration checklist. Each match → {game}/{stem}/: - match.json — metadata (vodurl, vodmatchstart, duration, recordstatus…

Access requested at publisher cc-by-nc-4.0 1K<n<10K

[View dataset](https://savrn.com/datasets/game-intention)

Dataset · Time series forecasting

### [tpc-raw-waveform-dumps](https://savrn.com/datasets/tpc-raw-waveform-dumps)

[Subatomic Physics & Detector Telemetry Lab (SPDTL)](https://savrn.com/model-publishers/subatomic-telemetry-lab)

This repository serves as a cold-storage archive for uncalibrated, high-entropy digitized waveforms collected from Time Projection Chamber (TPC) readout pads during high-luminosity particle collision runs. The dataset consists of continuous analog-to-digital converter (ADC) time-series samples captured across multi-channel front-end ASICs before zero-suppression or track reconstruction algorithms are applied. Because these raw dumps preserve all hardware-level thermal noise, baseline fluctuations, and beam-induced electromagnetic interference, they appear as unstructured binary payloads. This dataset is designed for benchmarking distributed hardware accelerators, developing AI-driven online…

Publicly accessible cc-by-nc-sa-4.0 100K<n<1M

[View dataset](https://savrn.com/datasets/tpc-raw-waveform-dumps)

Dataset

### [estuary](https://savrn.com/datasets/estuary)

[Douglas Dale Bell](https://savrn.com/model-publishers/douglasdbell8492)

Publicly accessible

[View dataset](https://savrn.com/datasets/estuary)

## Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

### [Embedding models for retrieval](https://savrn.com/collections/embedding-models-for-retrieval)

Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.

[View collection](https://savrn.com/collections/embedding-models-for-retrieval)

Collection · 4 entries

### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-on-one-accelerator)

Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.

[View collection](https://savrn.com/collections/models-that-run-on-one-accelerator)

Collection · 6 entries

### [Open-weight text models worth knowing](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.

[View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Collection · 3 entries

### [Speech and audio models](https://savrn.com/collections/speech-and-audio)

Recognition and synthesis models, grouped so the two directions are easy to compare.

[View collection](https://savrn.com/collections/speech-and-audio)

## Related SAVRN Research

The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.

SAVRN Index

### [What open models cost to run](https://savrn.com/ai-index/pricing/open-models)

The same open-weight model priced by every host that serves it, per million tokens.

Research Hub

### [Data center trackers and maps](https://savrn.com/data-center-research)

Moratoriums, permits, power, water and capital behind the facilities that run these models.

Method

### [How the Model Hub is built](https://savrn.com/model-hub/methodology)

Sources, evidence labels, refresh behaviour, and the limits of every comparison here.