{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-yarina--meta_kaggle_dataset_archive_2026-03-12","slug":"yarina--meta_kaggle_dataset_archive_2026-03-12","name":"Meta_Kaggle_Dataset_Archive_2026-03-12","type":"dataset","url":"https://huggingface.co/datasets/Yarina/Meta_Kaggle_Dataset_Archive_2026-03-12","page_url":"https://unfragile.ai/yarina--meta_kaggle_dataset_archive_2026-03-12","categories":["model-training"],"tags":["license:mit","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-yarina--meta_kaggle_dataset_archive_2026-03-12__cap_0","uri":"capability://data.processing.analysis.kaggle.competition.metadata.extraction.and.archival","name":"kaggle competition metadata extraction and archival","description":"Extracts and preserves structured metadata from Kaggle competitions including problem descriptions, evaluation metrics, submission requirements, and temporal data (launch dates, deadlines, prize pools). 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Implements a multi-dimensional indexing pattern that allows fast subset extraction for specific research questions or use-case matching without loading the entire archive into memory.","intents":["I want to find all computer vision competitions from 2023-2025 with prize pools over $50K","I need to identify beginner-friendly NLP competitions to recommend to junior data scientists","I want to analyze which competition domains have the highest participation rates"],"best_for":["Data scientists building personalized competition recommendation engines","Researchers studying domain-specific competition trends","Platform developers creating competition discovery interfaces"],"limitations":["Filtering is limited to metadata fields present in the archive — cannot filter by submission quality or participant skill distribution","No full-text search across competition descriptions — only categorical and structured field filtering","Temporal filtering is based on competition launch date, not participant activity patterns"],"requires":["HuggingFace Datasets library with filter/select methods","Python 3.8+","Familiarity with Parquet or Arrow columnar formats for efficient filtering"],"input_types":["filter criteria (dictionaries or query expressions)","structured metadata (competition domain, difficulty, prize pool, dates)"],"output_types":["filtered dataset subsets (DataFrames)","aggregated statistics (counts, distributions by category)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-yarina--meta_kaggle_dataset_archive_2026-03-12__cap_2","uri":"capability://data.processing.analysis.training.dataset.curation.for.ml.model.development","name":"training dataset curation for ml model development","description":"Provides curated subsets of competition metadata suitable for training supervised models that predict competition success metrics (participation, submission quality, completion rates). Implements stratified sampling and train/validation/test splitting patterns to ensure representative distributions across competition types, difficulty levels, and temporal periods.","intents":["I want to train a model to predict how many participants will join a new competition based on its metadata","I need to build a classifier that predicts whether a competition will meet its participation targets","I want to create a regression model estimating submission volume from competition features"],"best_for":["ML engineers building predictive models for competition platform optimization","Data scientists studying competition success factors","Platform teams forecasting resource requirements for new competitions"],"limitations":["Target variables (participation counts, submission volumes) may not be fully captured in metadata-only archive","Class imbalance likely exists across competition difficulty/domain — requires explicit balancing strategies","Temporal distribution may be skewed toward recent competitions, affecting historical trend modeling"],"requires":["HuggingFace Datasets library","scikit-learn or pandas for train/test splitting","Python 3.8+","Understanding of stratified sampling for imbalanced datasets"],"input_types":["competition metadata (features: domain, difficulty, prize pool, timeline, etc.)","target variables (if available: participation counts, submission volumes)"],"output_types":["train/validation/test dataset splits (DataFrames or Arrow tables)","feature matrices (numeric and categorical)","stratification reports (distribution summaries)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-yarina--meta_kaggle_dataset_archive_2026-03-12__cap_3","uri":"capability://data.processing.analysis.temporal.competition.trend.analysis","name":"temporal competition trend analysis","description":"Enables time-series analysis of competition metadata across the 2026-03-12 snapshot, supporting trend extraction, seasonality detection, and cohort analysis. Implements temporal bucketing patterns (by month, quarter, year) and rolling window aggregations to surface patterns in competition launch frequency, prize pool allocation, and domain popularity over time.","intents":["I want to identify seasonal patterns in when Kaggle launches competitions","I need to analyze how average prize pools have changed year-over-year","I want to track the growth of specific competition domains (e.g., NLP, computer vision) over time"],"best_for":["Platform analysts studying Kaggle's competition strategy evolution","Researchers analyzing data science ecosystem trends","Business intelligence teams forecasting competition volume and investment"],"limitations":["Analysis is limited to a single snapshot date (2026-03-12) — cannot detect real-time trends or ongoing competitions","Temporal granularity depends on metadata precision — may lack intra-day or hourly launch data","Cannot analyze participant behavior over time without submission-level data"],"requires":["HuggingFace Datasets library","pandas or polars for time-series operations","Python 3.8+","Datetime parsing libraries (built into pandas)"],"input_types":["competition metadata with temporal fields (launch date, deadline, creation date)"],"output_types":["time-series aggregations (counts, sums, averages by time period)","trend visualizations (line charts, heatmaps)","statistical summaries (growth rates, seasonality indices)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-yarina--meta_kaggle_dataset_archive_2026-03-12__cap_4","uri":"capability://data.processing.analysis.domain.and.category.based.competition.segmentation","name":"domain and category-based competition segmentation","description":"Segments the 413K+ competition archive into domain-specific subsets (computer vision, NLP, tabular data, time-series, etc.) using categorical metadata. Implements hierarchical categorization patterns that enable both broad domain analysis and fine-grained sub-category exploration, with support for multi-label assignments where competitions span multiple domains.","intents":["I want to analyze competition characteristics separately for NLP vs computer vision domains","I need to identify underrepresented competition types to recommend to platform stakeholders","I want to build domain-specific recommendation models with separate training data per category"],"best_for":["Domain specialists analyzing competition trends within their field","Platform product managers identifying gaps in competition portfolio","ML researchers studying domain-specific modeling approaches"],"limitations":["Domain categorization is based on metadata tags — may not capture nuanced problem types or hybrid domains","Multi-label competitions may be underrepresented if archive uses single-category assignment","Domain definitions may be inconsistent across competition creation dates"],"requires":["HuggingFace Datasets library","pandas for groupby operations","Python 3.8+","Understanding of the domain taxonomy used in the archive"],"input_types":["competition metadata with domain/category fields"],"output_types":["segmented datasets by domain (DataFrames)","domain statistics (competition counts, prize distributions, participation metrics)","domain-specific feature distributions"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-yarina--meta_kaggle_dataset_archive_2026-03-12__cap_5","uri":"capability://data.processing.analysis.prize.pool.and.incentive.structure.analysis","name":"prize pool and incentive structure analysis","description":"Extracts and analyzes prize pool data across competitions, enabling comparative analysis of incentive structures, reward distributions, and their correlation with participation/submission metrics. Implements aggregation patterns that normalize prize data across different currencies and time periods to enable fair cross-competition comparisons.","intents":["I want to understand how prize pool size correlates with competition participation","I need to analyze whether higher prizes lead to better solution quality","I want to benchmark prize allocations for a new competition I'm designing"],"best_for":["Competition designers optimizing incentive structures","Economists studying crowdsourcing incentive mechanisms","Platform stakeholders analyzing ROI of prize investments"],"limitations":["Prize data may be incomplete or missing for older competitions","Currency normalization requires historical exchange rates — snapshot may not reflect current values","Prize structure (e.g., distribution across top-N winners) may not be fully captured in metadata","Does not include non-monetary incentives (badges, reputation, portfolio value)"],"requires":["HuggingFace Datasets library","pandas for aggregation and analysis","Python 3.8+","Optional: currency conversion library for historical normalization"],"input_types":["competition metadata with prize pool fields (total prize, currency, distribution details)"],"output_types":["prize statistics (mean, median, distribution by domain)","correlation matrices (prize vs participation, submission quality)","comparative analysis tables (prize benchmarks by competition type)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-yarina--meta_kaggle_dataset_archive_2026-03-12__cap_6","uri":"capability://data.processing.analysis.reproducible.research.dataset.versioning.and.citation","name":"reproducible research dataset versioning and citation","description":"Provides versioned, citable access to the competition archive through HuggingFace's dataset versioning system, enabling reproducible research with guaranteed data consistency across time. Implements immutable snapshot patterns where each version is pinned to a specific commit hash, allowing researchers to reference exact dataset versions in publications and ensure other researchers can reproduce analyses.","intents":["I want to publish research using this dataset and ensure readers can access the exact same data version","I need to track how my analysis results change if I update to a newer version of the dataset","I want to cite this dataset in my academic paper with a persistent, versioned reference"],"best_for":["Academic researchers publishing peer-reviewed studies","Data scientists documenting reproducible analyses","Teams maintaining long-term research projects with evolving datasets"],"limitations":["Versioning is tied to HuggingFace Hub — requires internet access to fetch specific versions","Version history is limited to HuggingFace's retention policy — very old versions may be pruned","No built-in data validation — researchers must verify data integrity independently"],"requires":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.8+","HuggingFace Hub account (free) for accessing version metadata","Git knowledge for understanding commit-based versioning"],"input_types":["version identifiers (commit hash, tag, or 'main' for latest)"],"output_types":["versioned dataset snapshots (DataFrames, Arrow tables)","version metadata (commit hash, timestamp, changelog)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"low","permissions":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.8+","~50GB disk space for full dataset download","Internet connection for initial dataset fetch from HuggingFace Hub","HuggingFace Datasets library with filter/select methods","Familiarity with Parquet or Arrow columnar formats for efficient filtering","HuggingFace Datasets library","scikit-learn or pandas for train/test splitting","Understanding of stratified sampling for imbalanced datasets","pandas or polars for time-series operations"],"failure_modes":["Snapshot is fixed at 2026-03-12 — does not reflect real-time competition updates or new submissions after archival date","Metadata extraction may not capture all custom evaluation metrics or domain-specific competition rules","No participant-level data (submissions, scores, leaderboard rankings) — only competition-level metadata","Filtering is limited to metadata fields present in the archive — cannot filter by submission quality or participant skill distribution","No full-text search across competition descriptions — only categorical and structured field filtering","Temporal filtering is based on competition launch date, not participant activity patterns","Target variables (participation counts, submission volumes) may not be fully captured in metadata-only archive","Class imbalance likely exists across competition difficulty/domain — requires explicit balancing strategies","Temporal distribution may be skewed toward recent competitions, affecting historical trend modeling","Analysis is limited to a single snapshot date (2026-03-12) — cannot detect real-time trends or ongoing competitions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"ecosystem":0.36,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:48.064Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=yarina--meta_kaggle_dataset_archive_2026-03-12","compare_url":"https://unfragile.ai/compare?artifact=yarina--meta_kaggle_dataset_archive_2026-03-12"}},"signature":"7oBK5vKMvQLSGVgm7caKCtrtqe5yfFRJErK5OhI3jjOb9VHF/xrPD64bo625S++WwsIQkJxuLO72NpungUbOBg==","signedAt":"2026-06-20T21:48:48.858Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yarina--meta_kaggle_dataset_archive_2026-03-12","artifact":"https://unfragile.ai/yarina--meta_kaggle_dataset_archive_2026-03-12","verify":"https://unfragile.ai/api/v1/verify?slug=yarina--meta_kaggle_dataset_archive_2026-03-12","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}