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Users should avoid ecological harm, including misuse for sensitive species localization or unsupported deployment claims.", "includedInDataCatalog": { "@type": "DataCatalog", "name": "Earth Species Project synthetic bioacoustic datasets" }, "usageInfo": { "downstreamUse": "The dataset is intended for research on bioacoustic sound-event detection with diarization, overlapping vocalization attribution, audio representation learning, and pretraining or evaluation of models that identify and separate temporally localized animal-sound events in short audio scenes. It may also be used as synthetic audio input for constructing relational tasks about event order, speaker counts, and source attribution.", "outOfScopeUse": "The dataset consists of synthetic 10-second scenes and diarization labels generated from controlled synthetic composition. It should not be treated as a complete substitute for real multi-individual field recordings, where reverberation, movement, localization uncertainty, overlapping calls, and annotator ambiguity can be more complex. It is not recommended as the sole evidence for ecological deployment, individual-animal monitoring, conservation decisions, or policy decisions.", "biasRisksLimitations": { "bias": "The dataset inherits selection biases from the source recordings and synthetic scene-generation process. Diarization labels may reflect proxy speaker construction and the assumptions of the generator rather than the full range of real animal vocal interactions. Event density, overlap patterns, speaker balance, taxa, geography, recording conditions, and signal-to-noise ratio may be unevenly represented, and weak-label source noise may carry through as missing or imperfect event labels.", "risks": "Potential benefits include improved research tools for bioacoustic diarization, overlapping-call analysis, and biodiversity monitoring. Potential harms include overclaiming individual/source attribution capability in real deployments, using model outputs for sensitive species or population tracking without validation, or replacing expert ecological analysis. Users should validate in target domains and avoid sharing sensitive species or site-level information.", "limitations": "The dataset consists of synthetic 10-second scenes and diarization labels generated from controlled synthetic composition. It should not be treated as a complete substitute for real multi-individual field recordings, where reverberation, movement, localization uncertainty, overlapping calls, and annotator ambiguity can be more complex. It is not recommended as the sole evidence for ecological deployment, individual-animal monitoring, conservation decisions, or policy decisions." }, "safetyMitigations": "Validate trained models on real target-domain recordings, compare against expert annotations where possible, document known domain gaps, and avoid using model outputs as the sole basis for ecological or policy decisions.", "recommendations": "Use this dataset as research and training data alongside real-world validation sets. Report source-domain assumptions, taxonomic and acoustic coverage, and target-domain performance when publishing models trained on it." }, "rai:dataLimitations": "The dataset consists of synthetic 10-second scenes and diarization labels generated from controlled synthetic composition. It should not be treated as a complete substitute for real multi-individual field recordings, where reverberation, movement, localization uncertainty, overlapping calls, and annotator ambiguity can be more complex. It is not recommended as the sole evidence for ecological deployment, individual-animal monitoring, conservation decisions, or policy decisions.", "rai:dataBiases": "The dataset inherits selection biases from the source recordings and synthetic scene-generation process. Diarization labels may reflect proxy speaker construction and the assumptions of the generator rather than the full range of real animal vocal interactions. Event density, overlap patterns, speaker balance, taxa, geography, recording conditions, and signal-to-noise ratio may be unevenly represented, and weak-label source noise may carry through as missing or imperfect event labels.", "rai:personalSensitiveInformation": "No human personal data is intentionally included. The dataset is synthetic bioacoustic audio and annotation metadata. Some source recordings may have originated from real recording locations or field projects, so users should avoid attempting to infer or publicize sensitive species/site information unless explicitly permitted by source-data terms.", "rai:dataUseCases": "The dataset is intended for research on bioacoustic sound-event detection with diarization, overlapping vocalization attribution, audio representation learning, and pretraining or evaluation of models that identify and separate temporally localized animal-sound events in short audio scenes. It may also be used as synthetic audio input for constructing relational tasks about event order, speaker counts, and source attribution.", "rai:dataSocialImpact": "Potential benefits include improved research tools for bioacoustic diarization, overlapping-call analysis, and biodiversity monitoring. Potential harms include overclaiming individual/source attribution capability in real deployments, using model outputs for sensitive species or population tracking without validation, or replacing expert ecological analysis. Users should validate in target domains and avoid sharing sensitive species or site-level information.", "rai:hasSyntheticData": true, "rai:considerations": { "@type": "rai:Considerations", "rai:downstreamUse": "The dataset is intended for research on bioacoustic sound-event detection with diarization, overlapping vocalization attribution, audio representation learning, and pretraining or evaluation of models that identify and separate temporally localized animal-sound events in short audio scenes. It may also be used as synthetic audio input for constructing relational tasks about event order, speaker counts, and source attribution.", "rai:outOfScopeUse": "The dataset consists of synthetic 10-second scenes and diarization labels generated from controlled synthetic composition. It should not be treated as a complete substitute for real multi-individual field recordings, where reverberation, movement, localization uncertainty, overlapping calls, and annotator ambiguity can be more complex. It is not recommended as the sole evidence for ecological deployment, individual-animal monitoring, conservation decisions, or policy decisions.", "rai:knownBiases": "The dataset inherits selection biases from the source recordings and synthetic scene-generation process. Diarization labels may reflect proxy speaker construction and the assumptions of the generator rather than the full range of real animal vocal interactions. Event density, overlap patterns, speaker balance, taxa, geography, recording conditions, and signal-to-noise ratio may be unevenly represented, and weak-label source noise may carry through as missing or imperfect event labels.", "rai:potentialRisks": "Potential benefits include improved research tools for bioacoustic diarization, overlapping-call analysis, and biodiversity monitoring. Potential harms include overclaiming individual/source attribution capability in real deployments, using model outputs for sensitive species or population tracking without validation, or replacing expert ecological analysis. Users should validate in target domains and avoid sharing sensitive species or site-level information.", "rai:limitations": "The dataset consists of synthetic 10-second scenes and diarization labels generated from controlled synthetic composition. It should not be treated as a complete substitute for real multi-individual field recordings, where reverberation, movement, localization uncertainty, overlapping calls, and annotator ambiguity can be more complex. It is not recommended as the sole evidence for ecological deployment, individual-animal monitoring, conservation decisions, or policy decisions.", "rai:mitigationStrategies": "Use domain-specific validation, consult bioacoustics experts for high-stakes uses, document failures, and avoid deployment on sensitive species or sites without appropriate safeguards.", "rai:audience": "Researchers in machine learning, bioacoustics, ecology, and animal communication", "rai:usageGuidelines": "Do not treat synthetic-dataset performance as sufficient evidence for real-world ecological monitoring or individual/source attribution." }, "prov:wasGeneratedBy": { "@type": "prov:Activity", "name": "Synthetic bioacoustic scene generation and Hugging Face sharded export", "description": "Synthetic 10-second, 16 kHz scenes and selection tables were generated using heavy augmentation and weak-to-strong label conversion, then exported as WebDataset tar shards with a compact metadata.parquet table." }, "conformsTo": "http://mlcommons.org/croissant/1.1" }