AI Training Datasets

Synthetische, wertebasierte Erzaehltexte, generiert aus den Allgemeinen Erklaerung der Menschenrechte (UDHR, gemeinfrei) durch ein lokales Sprachmodell (Default qwen2.5:7b via Ollama). output (Erzaehltext), meta (Artikel, Stil, Sprache, Modell, Zeitstempel, Wortzahl, Guardrail-Pruefergebnis). landen im Quarantaene-Ast des Quell-Repositories und werden hier nicht veroeffentlicht. Der Audit ist ein Filter, kein Beweis — Nachpruefung empfohlen.

Dataset · Feature extraction

epstein-index

Robb Doering

Machine-readable text of the publicly released Jeffrey Epstein–related document collections: the DOJ "EFTA" disclosures (Data Sets 1–12), House Oversight releases, court records (Maxwell, Doe v. Epstein/Indyke, Florida v. Epstein, USVI v. JPMorgan, and others), and FOIA productions (FBI, BOP, CBP, Florida). All content is public-record material released by the U.S. Department of Justice, federal/state courts, and FOIA respondents. This is a living dataset: this snapshot publishes 32,172 documents — including the full House Oversight estate releases of Oct 17 (8,718 pages) and Nov 12 (22,903 pages); roughly 33,000 source PDFs are downloaded and mid-OCR at snapshot time, with over 1.3M…

Dataset · Text retrieval

ipfs_australia_laws_ir

Justicedao

Research retrieval release of endomorphosis/ipfsaustralialaws (revision c72ad1989fc8d4f4858ea8e2944c77f08406a6dd) packaged as country-laws-ir-graphrag/v1 (layout family skillcenter-huggingface-release/v3 / publicus-ir). Not legal advice. This is a research snapshot. The official gazette / authentic source of Australia prevails over this corpus. Retrieved documents and graph edges are retrieval evidence only. No legal text was invented. Primary key: entrycid (CIDv1 raw sha2-256 of a canonical identity record). Integer documentindex values are compact shard pointers, not identities. Target Hub id (packaging metadata only): justicedao/ipfsaustralialawsir. entrycid, lawcid, recordtype…

Dataset

Dream.exe

Kaiming Yang

Dream.exe Benchmark Code · DVD checkpoints This release preserves the original 101 benchmark cases. The Dream.exe authors constructed the task instances, frozen scenes, camera settings, generation inputs, and reference data using RoboCasa data and environments. Download and reproduce After completing the code repository's installation guide, run from the code repository root: hf download kaimingyang/Dream.exe --repo-type dataset \ --include 'bench/'

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…

Dataset · Object detection

tracking_anything_datasets

Wen

Release: tracking-anything-native-corpora-20261004. Upload: IN PROGRESS — do not treat partial uploads as a complete dataset. Original native archive/annotation mirror, not converted conversations or model-generated labels. No model weights, V8 memory cache, credentials, cookies, or runtime logs are included. This is not a strictly train-only export. TNL2K/VastTrack standalone test archives are excluded; LaSOT and TNLLT originals contain both train and test. COCO train2014 includes images used by REFER training, validation and test expressions. Use the original published splits; never train indiscriminately on every archive member. The small official TNL2K four-video patch also contains one…

Every page in this dataset was read by a Kairo spider in a real browser (watch them live at https://kairollm.live), cleaned (menus/footers removed), checked for crypto relevance and deduplicated (SHA-256 + SimHash). It is updated automatically while the spiders work, and each Kairo model version names the exact revision of this dataset it was trained on. id (increasing), url, domain, chapter, title, text, tokens (approximate), crawledat (ms). Pages keep the copyright of their original sites; this dataset is shared for research.

Dataset · Image feature extraction

openmind

NeuroSpin/GAIA

Preprocessed versions of the OpenMind dataset (Wald et al., 2025): head & neck 3D MRI pooled from ~800 OpenNeuro datasets. Here the brain T1w, T2w and FLAIR images were preprocessed at using brainprep and packed as WebDataset shards, ready for large-scale (e.g. self-supervised) deep learning. All folders share the same sample identifiers: (datasetid, subject, session, run), with datasetid the shard folder name, so derivatives of the same scan can be joined across folders. Each preprocessing folder holds one folder of WebDataset shards per OpenNeuro dataset and an index.json.gz with the byte offsets of every file of the shards, used by nidl for random access. valsplit.csv (columns datasetid…