datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
harbor-goose-openhands-benchmark
Same Model, Opposite Results: Goose vs OpenHands Turn Budget Study on Harbor Terminal-Bench-Pro
Trial-level results from a small controlled study comparing two agent harnesses —
Goose and OpenHands-SDK —
on a frozen 40-task Harbor Terminal-Bench-Pro slice.
All runs used minimax/minimax-m2.5 via OpenRouter with Daytona as the sandbox backend.
Key Findings
Reducing the turn budget from 100 to 60 pushed the two harnesses in opposite directions under the base setup:… See the full description on the dataset page: https://huggingface.co/datasets/namanvats/harbor-goose-openhands-benchmark.GooseReason-0.7M
Xent GooseReason 0.7M
This is a reproducibly shuffled and benchmark-decontaminated derivative of
nvidia/Nemotron-Research-GooseReason-0.7M.
Splits
Each source subset has a training split plus validation and test splits containing
500 rows each. Holdouts are stratified by num_choices with largest-remainder
allocation, then every split is deterministically shuffled with seed 42.
Source subset
Original
Raw exact
Added by normalization
Invalid/mask removed… See the full description on the dataset page: https://huggingface.co/datasets/xent-labs/GooseReason-0.7M.Nemotron-Research-GooseReason-0.7M
GooseReason-0.7M
Synthesized with Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text
GooseReason-0.7M is a large-scale RLVR dataset with over 0.7 million tasks across mathematics, programming, and general scientific domains, synthesized by the Golden Goose pipeline. It is used to train GooseReason-4B-Instruct, which achieves new state-of-the-art results among 4B-Instruct models across 15 diverse benchmarks, spanning mathematics… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Research-GooseReason-0.7M.RWKV-World-v3
RWKV-7 (Goose) World v3 Corpus
Paper | Code
This is an itemised and annotated list of the RWKV World v3 corpus
which is a multilingual dataset with about 3.1T tokens used to train the
"Goose" RWKV-7 World model series.
RWKV World v3 was crafted from public datasets spanning >100 world languages
(80% English, 10% multilang, and 10% code). Also available as a HF Collection of Datasets.
Subsampled subsets (previews) of the corpus are available as 100k JSONL dataset and 1M JSONL dataset… See the full description on the dataset page: https://huggingface.co/datasets/Goose-World/RWKV-World-v3.tianjin-pm25-dataclinical-trial-outcomes-2020plus
Clinical Trial Outcomes (2020+) with Normalized Endpoints
124,790 normalized endpoints across 14,170 clinical studies that started on or after
2020-01-01 and have posted results on ClinicalTrials.gov.
Snapshot: 2026-09-08. Source: ClinicalTrials.gov API v2 (U.S. National Library of Medicine).
Built with ctgov — the same normalizer, released as an
MIT-licensed package with zero dependencies. So this snapshot is not a dead artifact: you can
re-run it against the live registry, or… See the full description on the dataset page: https://huggingface.co/datasets/GooseWithStories/clinical-trial-outcomes-2020plus.mussel-gooseneck-seg-rgb-640
MusselGooseneckSeg: Semantic Segmentation for Rocky Intertidal Mussel and Gooseneck Barnacle Habitat
Dataset description
MusselGooseneckSeg is a dataset for semantic segmentation of mussel and gooseneck barnacle habitat using high resolution drone imagery. It provides pixel-wise annotation for mussels and gooseneck barnacles in rocky intertidal zones.
Source: Imagery collected by the Hakai Institute
Task description
The dataset is designed for semantic… See the full description on the dataset page: https://huggingface.co/datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-640.transformers-coding-session-goose-traces
dacorvo/transformers-coding-session-goose-traces
goose coding-agent session traces produced by
agentcap runs. Each run
contributes one folder under data/<run_id>/; inside, one file per
session in goose's native export format.
The on-the-wire HTTP captures for these same runs live in
dacorvo/transformers-coding-session-captures.
Both belong to the
transformers-coding-session Collection
— join on run_id to align captures with traces.
Goose-5Labels-resizedgoldengoose-divsweepv2_lowdiv_goose_n512_grouporc_tau1.00_n7goldengoose-p3_goose_lowdiv_n128_indoc_tau1.00-25grpadaption-goose-governance-broad-seed-v1-augmented
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-goose_governance_broad_seed_v1 (augmented)
An English instruction-tuning dataset covering core aspects of governance, including political systems, public policy, international relations, and security. Prompts span multiple task types such as conceptual inquiries, comparative institutional analyses, policy trade-off assessments, and evidence synthesis. Completions provide neutral… See the full description on the dataset page: https://huggingface.co/datasets/darthludious/adaption-goose-governance-broad-seed-v1-augmented.goldengoose-divsweepv2_goose_n128_indorc_tau0.50_n25goldengoose-divsweep_goose_n128_indorc_tau0.70_gumbel07-25grphf-hub-session-goose-traces
dacorvo/hf-hub-session-goose-traces
goose coding-agent session traces produced by
agentcap runs. Each run
contributes one folder under data/<run_id>/; inside, one file per
session in goose's native export format.
The on-the-wire HTTP captures for these same runs live in
dacorvo/hf-hub-session-captures.
Both belong to the
hf-hub-session Collection
— join on run_id to align captures with traces.
goldengoose-divsweep_goose_n512_indorc_tau0.30_gumbel03-7grpgoldengoose-divsweep_goose_n128_grouporc_tau0.30_gumbel03-25grpgoldengoose-p3_goose_highdiv_n128_indoc_tau1.00-25grpgoldengoose-divsweepv2_lowdiv_goose_n128_indorc_seed200_tau0.10_n25goldengoose-p3_goose_highdiv_n128_grpoc_tau1.00-25grpgoldengoose-p3fu_goose_highdiv_n128_grpoc_tau0.10_seed200-25grpuntitled_goose_game
Dataset Card for "untitled_goose_game"
More Information needed
goldengoose-divsweep_goose_n128_indorc_tau0.10_seed200-25grpgoldengoose-divsweepv2_lowdiv_goose_n512_seed100_random_n7goldengoose-p3_goose_lowdiv_n128_grpoc_tau0.10-25grpgoldengoose-p3fu_goose_highdiv_n128_indoc_tau0.10_seed200-25grpgoldengoose-divsweepv2_goose_n512_indorc_tau0.10_n7goldengoose-divsweep_goose_n512_indorc_tau0.50_seed100-7grpgoldengoose-divsweep_goose_n128_indorc_tau0.30_gumbel03-25grpimnet1k_goose