{
  "@context": {
    "@language": "en",
    "@vocab": "https://schema.org/",
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    "rai": "http://mlcommons.org/croissant/RAI/",
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    "dataBiases": "cr:dataBiases",
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    "annotatorDemographics": "cr:annotatorDemographics",
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  },
  "@type": "sc:Dataset",
  "name": "MMTT-Bench",
  "description": "MMTT-Bench (Multimodal Text-and-Time-series Benchmark) is a synthetic benchmark for evaluating information-theoretic metrics on text-annotated time series. It provides controlled experimental conditions for measuring how much textual annotations inform predictions about co-occurring time-series signals, enabling rigorous evaluation of metrics including KSG mutual information, partial information decomposition (PID), V-information, and pointwise mutual information (PMI). The synthetic signals is a sine wave with constant-period discontinuities, annoted with correct, incorrect and irrelevant text annotations",
  "conformsTo": "http://mlcommons.org/croissant/1.0",
  "citeAs": "@dataset{mmtt_bench_2026, title={When Does Text Inform? Benchmarking Information-Theoretic Metrics for Multimodal Time-Series Forecasting}, year={2026}}",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "url": "https://huggingface.co/datasets/WhenDoesTextInform/MMTT-Bench",
  "version": "1.0.0",
  "keywords": [
    "multimodal",
    "time series",
    "fusion",
    "forecasting",
    "information theory",
    "mutual information",
    "benchmark",
    "synthetic",
    "fusion"
  ],
  "inLanguage": "en",
  "isLiveDataset": false,

  "rai:dataCollection": "All data is synthetically generated. Time-series signals are procedurally constructed (sine wave with controlled constant-period discontinuities; Rössler chaotic attractor). Text annotations are generated algorithmically to encode varying degrees of mutual information with the time-series signals, providing ground-truth control over the information-theoretic relationships being benchmarked.",
  "rai:dataCollectionType": "Synthetic / programmatically generated",
  "rai:dataCollectionMissingData": "No missing data. The benchmark is fully synthetic and all fields are populated by construction.",
  "rai:dataCollectionRawData": "There is no upstream raw data source. Signals and annotations are generated in Python using controlled random seeds. Source code for data generation is available in the accompanying paper repository.",
  "rai:dataCollectionTimeframe": "Generated in 2026 for submission to NeurIPS 2026 Evaluations and Datasets Track.",
  "rai:dataImputationProtocol": "Not applicable. No real-world data was collected and no imputation was required.",
  "rai:dataPreprocessingProtocol": "Time-series values are normalised per signal type prior to serialisation. Text annotations are generated at the segment level and serialised as string fields alongside the corresponding numeric time-series arrays. No further preprocessing is applied; downstream metric implementations are expected to operate on the values as stored and implement preprocessing appropriate to their constraints, like text embeddings and dimensionality reduction.",
  "rai:dataManipulationProtocol": "Dataset splits (train / val / test) are constructed temporally by cycle, with training consisting of the first 62.5% of the signal, validation the next 18.75% and testing the final 18.75%.",
  "rai:dataAnnotationProtocol": "Annotations are machine-generated. Each time-series segment is paired with a text annotation whose information content with respect to the segment is set by a controlled parameter. There are no human annotators.",
  "rai:dataAnnotationPlatform": "Custom Python scripts (no external annotation platform).",
  "rai:dataAnnotationAnalysis": "Mutual information between text annotations and time-series segments is true by design of the dataset, and analytically verified through MI estimators and model performance.",
  "rai:annotationsPerItem": "1 text annotation per text category (correct, incorrect, irrelevant) per time-series point. Annotations are stored as a list to enable extension to multiple text samples per category per point.",
  "rai:annotatorDemographics": "Not applicable. All annotations are machine-generated.",
  "rai:machineAnnotationTools": "Custom Python data-generation scripts. No third-party annotation models (e.g., LLMs) were used to produce the annotations.",
  "rai:dataBiases": "As a fully synthetic benchmark, MMTT-Bench does not reflect biases present in naturally occurring text or time-series corpora. However, the benchmark is intentionally narrow in scope: signal diversity is limited to a single signal, and text annotation vocabulary is controlled. Results on MMTT-Bench may not generalise to real-world multimodal datasets with organic language variation or distribution shift.",
  "rai:dataUseCases": "Intended use: controlled evaluation and comparison of information-theoretic metrics (KSG mutual information, PID, V-information, PMI) on multimodal data pairing text with time series. Suitable for benchmarking metric sensitivity, bias, and computational properties under known ground-truth conditions. Not intended for deployment in production systems or for training general-purpose models.",
  "rai:dataLimitations": "The benchmark covers only one signal types. Text annotations follow a constrained vocabulary and do not reflect the complexity of natural language. Metric evaluation results should be interpreted within the synthetic setting and not extrapolated to real-world multimodal corpora without further validation.",
  "rai:dataSocialImpact": "MMTT-Bench contains no personal data, sensitive content, or real-world subjects. It is a methodological contribution intended to improve the rigour of information-theoretic metric evaluation in the ML research community. No direct negative societal impact is anticipated.",
  "rai:personalSensitiveInformation": "None. The dataset is entirely synthetic and contains no personal, demographic, medical, or otherwise sensitive information.",
  "rai:dataReleaseMaintenance": "The dataset is released as a static benchmark under CC-BY 4.0. Versioned releases will be maintained on Hugging Face. Bug reports and questions can be raised via the Hugging Face community discussion tab.",
  "rai:hasSyntheticData": true,
  "prov:wasDerivedFrom": [
    {
      "@id": "https://anonymous.4open.science/r/MMTT-Bench-0C83/src/mmtt_bench/data_generation/sine_templates.py",
      "prov:label": "Templated text annoations, templates in MMTT-Bench code",
      "sc:license": "MIT"
    }
  ],
  "prov:wasGeneratedBy": [
    {
      "@type": "prov:Activity",
      "prov:type": {
        "@id": "https://www.wikidata.org/wiki/Q655427"
      },
      "prov:label": "Signal generation",
      "sc:description": "Time-series signals are procedurally generated using deterministic algorithms: a sine wave with controlled constant-period discontinuities. Ground-truth labels (phase, true_direction, true_magnitude) are derived analytically from signal values at each timestep."
    },
    {
      "@type": "prov:Activity",
      "prov:type": {
        "@id": "https://www.wikidata.org/wiki/Q109719325"
      },
      "prov:label": "Template-based annotation",
      "sc:description": "Natural language annotations are produced by a template-based Python text generator. Slot values (direction_word, magnitude_word, tense) are drawn from a controlled vocabulary and filled into sentence templates to produce one or more candidate texts per point. Distractor annotations (category='incorrect') are generated by the same mechanism with deliberately mismatched slot values. No language models, crowdworkers, or third-party annotation platforms were involved."
    }
  ],

  "distribution": [
    {
      "@type": "cr:FileObject",
      "@id": "train-json",
      "name": "train.json",
      "description": "Training split. A single JSON object with two top-level keys: 'metadata' (split='train', n_points=3840, n_samples=1, and a 't' array of timestamps) and 'points' (array of per-point records each containing signal values, ground-truth labels, and text annotations).",
      "contentUrl": "https://huggingface.co/datasets/WhenDoesTextInform/MMTT-Bench/resolve/main/train.json",
      "encodingFormat": "application/json",
      "sha256": "831b4ce98d414d1efe137c1a0716246f85b2f434ab491b956901519dc5ce4243"
    },
    {
      "@type": "cr:FileObject",
      "@id": "val-json",
      "name": "val.json",
      "description": "Validation split. Same format as train.json: a single JSON object with 'metadata' (split='val', n_points=1152) and 'points' array.",
      "contentUrl": "https://huggingface.co/datasets/WhenDoesTextInform/MMTT-Bench/resolve/main/val.json",
      "encodingFormat": "application/json",
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    },
    {
      "@type": "cr:FileObject",
      "@id": "test-json",
      "name": "test.json",
      "description": "Test split. Same format as train.json: a single JSON object with 'metadata' (split='test', n_points=1152) and 'points' array.",
      "contentUrl": "https://huggingface.co/datasets/WhenDoesTextInform/MMTT-Bench/resolve/main/test.json",
      "encodingFormat": "application/json",
      "sha256": "c6fce1fc780bdc94df516c06871c6b61627ec96e80a29a945cf4752deac1674c"
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  "recordSet": [
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    "@type": "cr:RecordSet",
    "@id": "train-records",
    "name": "train",
    "description": "Training split. One record per point in the time series.",
    "field": [
      {
        "@type": "cr:Field",
        "@id": "train-records/t",
        "name": "t",
        "description": "Timestamp of the point.",
        "dataType": "sc:Float",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].t" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "train-records/y",
        "name": "y",
        "description": "Signal value at this point.",
        "dataType": "sc:Float",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].y" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "train-records/dy",
        "name": "dy",
        "description": "Derivative of the signal at this point.",
        "dataType": "sc:Float",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].dy" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "train-records/phase",
        "name": "phase",
        "description": "Phase label for the point (e.g. ascending_zero).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].phase" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "train-records/true_direction",
        "name": "true_direction",
        "description": "Ground-truth direction label (e.g. increasing/decreasing).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].true_direction" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "train-records/true_magnitude",
        "name": "true_magnitude",
        "description": "Ground-truth magnitude label (e.g. steep/gradual).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].true_magnitude" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "train-records/category",
        "name": "category",
        "description": "Whether the text annotation is correct, incorrect or irrelevant",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].category" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "train-records/tense",
        "name": "tense",
        "description": "Grammatical tense of the annotation (past/present/future).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].tense" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "train-records/text",
        "name": "text",
        "description": "Natural language annotation for this point (first sample).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].samples[0].text" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "train-records/irrelevant_group",
        "name": "irrelevant_group",
        "description": "Distractor group identifier; null for correct annotations.",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "train-json" },
          "extract": { "jsonPath": "$.points[*].samples[0].irrelevant_group" }
        }
      }
    ]
    },
    {
    "@type": "cr:RecordSet",
    "@id": "val-records",
    "name": "val",
    "description": "Validation split. One record per point in the time series.",
    "field": [
      {
        "@type": "cr:Field",
        "@id": "val-records/t",
        "name": "t",
        "description": "Timestamp of the point.",
        "dataType": "sc:Float",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].t" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "val-records/y",
        "name": "y",
        "description": "Signal value at this point.",
        "dataType": "sc:Float",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].y" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "val-records/dy",
        "name": "dy",
        "description": "Derivative of the signal at this point.",
        "dataType": "sc:Float",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].dy" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "val-records/phase",
        "name": "phase",
        "description": "Phase label for the point (e.g. ascending_zero).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].phase" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "val-records/true_direction",
        "name": "true_direction",
        "description": "Ground-truth direction label (e.g. increasing/decreasing).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].true_direction" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "val-records/true_magnitude",
        "name": "true_magnitude",
        "description": "Ground-truth magnitude label (e.g. steep/gradual).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].true_magnitude" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "val-records/category",
        "name": "category",
        "description": "Whether the text annotation is correct, incorrect or irrelevant.",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].category" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "val-records/tense",
        "name": "tense",
        "description": "Grammatical tense of the annotation (past/present/future).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].tense" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "val-records/text",
        "name": "text",
        "description": "Natural language annotation for this point (first sample).",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].samples[0].text" }
        }
      },
      {
        "@type": "cr:Field",
        "@id": "val-records/irrelevant_group",
        "name": "irrelevant_group",
        "description": "Distractor group identifier; null for correct annotations.",
        "dataType": "sc:Text",
        "source": {
          "fileObject": { "@id": "val-json" },
          "extract": { "jsonPath": "$.points[*].samples[0].irrelevant_group" }
        }
      }
    ]
    },
    {
      "@type": "cr:RecordSet",
      "@id": "test-records",
      "name": "test",
      "description": "Test split. One record per point in the time series.",
      "field": [
        {
          "@type": "cr:Field",
          "@id": "test-records/t",
          "name": "t",
          "description": "Timestamp of the point.",
          "dataType": "sc:Float",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].t" }
          }
        },
        {
          "@type": "cr:Field",
          "@id": "test-records/y",
          "name": "y",
          "description": "Signal value at this point.",
          "dataType": "sc:Float",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].y" }
          }
        },
        {
          "@type": "cr:Field",
          "@id": "test-records/dy",
          "name": "dy",
          "description": "Derivative of the signal at this point.",
          "dataType": "sc:Float",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].dy" }
          }
        },
        {
          "@type": "cr:Field",
          "@id": "test-records/phase",
          "name": "phase",
          "description": "Phase label for the point (e.g. ascending_zero).",
          "dataType": "sc:Text",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].phase" }
          }
        },
        {
          "@type": "cr:Field",
          "@id": "test-records/true_direction",
          "name": "true_direction",
          "description": "Ground-truth direction label (e.g. increasing/decreasing).",
          "dataType": "sc:Text",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].true_direction" }
          }
        },
        {
          "@type": "cr:Field",
          "@id": "test-records/true_magnitude",
          "name": "true_magnitude",
          "description": "Ground-truth magnitude label (e.g. steep/gradual).",
          "dataType": "sc:Text",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].true_magnitude" }
          }
        },
        {
          "@type": "cr:Field",
          "@id": "test-records/category",
          "name": "category",
          "description": "Whether the text annotation is correct, incorrect or irrelevant.",
          "dataType": "sc:Text",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].category" }
          }
        },
        {
          "@type": "cr:Field",
          "@id": "test-records/tense",
          "name": "tense",
          "description": "Grammatical tense of the annotation (past/present/future).",
          "dataType": "sc:Text",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].tense" }
          }
        },
        {
          "@type": "cr:Field",
          "@id": "test-records/text",
          "name": "text",
          "description": "Natural language annotation for this point (first sample).",
          "dataType": "sc:Text",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].samples[0].text" }
          }
        },
        {
          "@type": "cr:Field",
          "@id": "test-records/irrelevant_group",
          "name": "irrelevant_group",
          "description": "Distractor group identifier; null for correct annotations.",
          "dataType": "sc:Text",
          "source": {
            "fileObject": { "@id": "test-json" },
            "extract": { "jsonPath": "$.points[*].samples[0].irrelevant_group" }
          }
        }
      ]
    }
  ]
}