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The dataset is pre-processed and versioned on HuggingFace infrastructure, eliminating the need for manual data collection, cleaning, or storage management. Supports both full-download and streaming modes for memory-constrained environments.","intents":["Load pre-cleaned US regional data into a machine learning pipeline without manual ETL","Access geospatial region boundaries, metadata, and administrative divisions for model training","Stream large regional datasets in batches without loading entire dataset into memory","Integrate standardized regional data into research or production workflows with version control"],"best_for":["ML researchers training location-aware models on US data","Data scientists building regional segmentation or clustering models","Teams prototyping geospatial applications without custom data pipelines"],"limitations":["US-only coverage — no international regional data included","Dataset versioning tied to HuggingFace releases — no guarantee of backward compatibility across major versions","No built-in data validation or quality metrics — assumes upstream curation is correct","Streaming mode requires persistent network connection; offline use requires pre-download","Unknown schema documentation depth — may require reverse-engineering column meanings from raw data"],"requires":["Python 3.7+","huggingface_hub library (pip install huggingface-hub)","datasets library (pip install datasets)","Internet connection for initial download or streaming","~500MB-2GB disk space depending on format (full dataset size unknown from metadata)"],"input_types":["None — dataset is self-contained; no external input required"],"output_types":["pandas.DataFrame","pyarrow.Table","numpy.ndarray","HuggingFace Dataset object (dict-like with lazy loading)"],"categories":["data-processing-analysis","geospatial-data"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-world-igr-plum--regions__cap_1","uri":"capability://data.processing.analysis.regional.metadata.extraction.and.schema.introspection","name":"regional metadata extraction and schema introspection","description":"Exposes dataset schema, column names, data types, and record counts through HuggingFace's dataset introspection API without downloading the full dataset. Enables developers to inspect what regional attributes are available (e.g., FIPS codes, population, boundaries) before committing to a download. Uses lazy metadata loading to provide instant schema visibility.","intents":["Inspect available regional attributes and data types before downloading","Determine if dataset contains specific geographic identifiers (FIPS, state codes, etc.)","Validate schema compatibility with downstream models or ETL pipelines","Understand dataset size and structure for memory planning"],"best_for":["Data engineers evaluating dataset fitness before integration","Researchers prototyping workflows and needing quick schema validation","Teams building automated data pipelines that need to adapt to schema changes"],"limitations":["Metadata-only — does not provide sample data or statistical summaries without full download","No column-level documentation or semantic meaning — schema names alone may be cryptic","Unknown whether dataset includes data quality metrics or null-value statistics","Introspection API depends on HuggingFace's metadata service availability"],"requires":["Python 3.7+","huggingface_hub library","Internet connection to reach HuggingFace metadata API"],"input_types":["None — introspection is read-only"],"output_types":["Python dict with schema (column names, types)","Integer (record count)","String (dataset description)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-world-igr-plum--regions__cap_2","uri":"capability://data.processing.analysis.version.controlled.dataset.snapshots.and.reproducible.data.loading","name":"version-controlled dataset snapshots and reproducible data loading","description":"Provides version pinning and reproducible loading through HuggingFace's dataset versioning system, allowing teams to lock to specific dataset versions (via git commit hashes or release tags) and ensure consistent data across training runs, environments, and team members. Caching is handled transparently by the datasets library, storing downloaded versions locally with integrity verification.","intents":["Ensure reproducible model training by pinning dataset version across team and time","Audit which dataset version was used for a specific model checkpoint","Safely update to new dataset versions without breaking existing pipelines","Share exact dataset snapshots in research papers or model cards"],"best_for":["ML teams requiring reproducible research and audit trails","Production systems where data consistency is critical","Research groups publishing models and needing to document exact data versions"],"limitations":["Version history depends on HuggingFace's git-based storage — no guarantee of indefinite retention","Pinning requires explicit version specification in code — no automatic version detection","Unknown whether dataset maintainers provide semantic versioning or changelog documentation","Cache invalidation and storage cleanup are manual — no automatic garbage collection"],"requires":["Python 3.7+","datasets library with version support","Knowledge of target dataset version (commit hash or tag)"],"input_types":["String (version identifier: commit hash, tag, or 'main')"],"output_types":["HuggingFace Dataset object pinned to specific version"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-world-igr-plum--regions__cap_3","uri":"capability://data.processing.analysis.distributed.dataset.splitting.and.train.test.partitioning","name":"distributed dataset splitting and train/test partitioning","description":"Supports deterministic train/validation/test splits using the datasets library's built-in split functionality, with configurable proportions and random seed control for reproducibility. Splits are computed lazily without materializing the full dataset, enabling efficient partitioning of large regional datasets across multiple machines or training runs. Supports both stratified and random splitting strategies.","intents":["Create reproducible train/validation/test splits for model evaluation","Partition regional data by geographic criteria or random sampling","Generate multiple cross-validation folds without duplicating data","Ensure consistent splits across distributed training jobs"],"best_for":["ML practitioners building supervised models on regional data","Teams running distributed training with consistent data partitioning","Researchers conducting cross-validation studies"],"limitations":["Splitting strategies are limited to random and basic stratification — no geographic stratification (e.g., by region type)","No built-in handling of data leakage across splits — assumes clean, independent records","Unknown whether splits preserve regional distribution or can be biased toward certain areas","Lazy splitting may cause memory spikes if splits are materialized all at once"],"requires":["Python 3.7+","datasets library","Specification of split proportions (e.g., 0.8/0.1/0.1)"],"input_types":["Float (train proportion, e.g., 0.8)","Float (validation proportion, e.g., 0.1)","Integer (random seed for reproducibility)"],"output_types":["Multiple HuggingFace Dataset objects (train, validation, test)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-world-igr-plum--regions__cap_4","uri":"capability://data.processing.analysis.batch.processing.and.format.conversion.for.downstream.ml.frameworks","name":"batch processing and format conversion for downstream ml frameworks","description":"Converts regional dataset into native formats for popular ML frameworks (PyTorch DataLoader, TensorFlow tf.data.Dataset, pandas DataFrame) through the datasets library's built-in conversion methods. Supports batching, shuffling, and collation without writing custom data loaders. Handles automatic type casting and tensor conversion for neural network training.","intents":["Convert regional dataset to PyTorch DataLoader for neural network training","Export data to TensorFlow tf.data.Dataset for distributed training","Transform to pandas DataFrame for statistical analysis or feature engineering","Apply custom collation functions for region-specific preprocessing"],"best_for":["ML engineers training deep learning models on regional data","Teams using PyTorch or TensorFlow for geospatial tasks","Data scientists performing exploratory analysis with pandas"],"limitations":["Format conversion may introduce type mismatches if dataset schema is ambiguous","Batching is generic — no built-in region-aware batching (e.g., grouping by state)","Collation functions require custom code for domain-specific preprocessing","Unknown whether conversion preserves data integrity or introduces rounding errors for numeric types"],"requires":["Python 3.7+","datasets library","Target framework (torch, tensorflow, or pandas) installed"],"input_types":["HuggingFace Dataset object","Integer (batch size)","Boolean (shuffle flag)"],"output_types":["torch.utils.data.DataLoader","tf.data.Dataset","pandas.DataFrame"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-world-igr-plum--regions__cap_5","uri":"capability://data.processing.analysis.mit.licensed.open.source.data.for.unrestricted.commercial.and.research.use","name":"mit-licensed open-source data for unrestricted commercial and research use","description":"Dataset is published under MIT license, permitting unrestricted use in commercial products, research, and derivative works with minimal attribution requirements. License is enforced through HuggingFace's license metadata system, enabling automated compliance checking in data pipelines. No usage restrictions, no commercial licensing fees, no data residency requirements.","intents":["Use regional data in commercial products without licensing negotiations","Publish research using this data without legal review","Create derivative datasets or models without attribution burden","Integrate into open-source projects with compatible licensing"],"best_for":["Startups and commercial teams building geospatial products","Academic researchers publishing papers","Open-source projects requiring permissive data licenses"],"limitations":["MIT license provides no warranty — dataset maintainers are not liable for data quality or accuracy","No SLA or support guarantees — dataset may be abandoned or removed","Attribution is required but minimal — must include license text in distributions","Unknown whether dataset contains any proprietary or restricted-use data that should not be MIT-licensed"],"requires":["Inclusion of MIT license text in derivative works","No other legal prerequisites"],"input_types":["None — license is metadata"],"output_types":["License compliance documentation"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","huggingface_hub library (pip install huggingface-hub)","datasets library (pip install datasets)","Internet connection for initial download or streaming","~500MB-2GB disk space depending on format (full dataset size unknown from metadata)","huggingface_hub library","Internet connection to reach HuggingFace metadata API","datasets library with version support","Knowledge of target dataset version (commit hash or tag)","datasets library"],"failure_modes":["US-only coverage — no international regional data included","Dataset versioning tied to HuggingFace releases — no guarantee of backward compatibility across major versions","No built-in data validation or quality metrics — assumes upstream curation is correct","Streaming mode requires persistent network connection; offline use requires pre-download","Unknown schema documentation depth — may require reverse-engineering column meanings from raw data","Metadata-only — does not provide sample data or statistical summaries without full download","No column-level documentation or semantic meaning — schema names alone may be cryptic","Unknown whether dataset includes data quality metrics or null-value statistics","Introspection API depends on HuggingFace's metadata service availability","Version history depends on HuggingFace's git-based storage — no guarantee of indefinite retention","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"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=world-igr-plum--regions","compare_url":"https://unfragile.ai/compare?artifact=world-igr-plum--regions"}},"signature":"CgiAUCIph5o8ApSMA3RL7n/E2Aq/SW5zfHce2F7ioGnUjDIM5nqC85Tt0ZB0SXE3gNin2T+JgwI0Em75WdP9BA==","signedAt":"2026-06-20T10:52:17.506Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/world-igr-plum--regions","artifact":"https://unfragile.ai/world-igr-plum--regions","verify":"https://unfragile.ai/api/v1/verify?slug=world-igr-plum--regions","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"}}