Duplicate from DLDLD111/IntraBench

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+configs:
+- config_name: engineering_human_factor
+  data_files:
+  - split: train
+    path: data/engineering_human_factor.json
+- config_name: material_science_additive_manufacturing
+  data_files:
+  - split: train
+    path: data/material_science_additive_manufacturing.json
+- config_name: physics_surface_enhanced_raman_spectroscopy
+  data_files:
+  - split: train
+    path: data/physics_surface_enhanced_raman_spectroscopy.json
+- config_name: public_health_infectious_disease_modeling
+  data_files:
+  - split: train
+    path: data/public_health_infectious_disease_modeling.json
+- config_name: earth_science_remote_sensing
+  data_files:
+  - split: train
+    path: data/earth_science_remote_sensing.json
+pretty_name: Intrabench
+task_categories:
+- question-answering
+- text-classification
+size_categories:
+- n<1K
+license: mit
+language:
+- en
+---
+
+# IntraBench Spaceholding
+
+This dataset is a five-domain benchmark of multiple-choice research questions derived from scientific literature.
+Each example represents one question-paper pair with a gold letter answer and supporting metadata.
+
+## Subsets
+- `engineering_human_factor`: Engineering - Human Factor
+- `material_science_additive_manufacturing`: Material Science - Additive Manufacturing
+- `physics_surface_enhanced_raman_spectroscopy`: Physics - Surface Enhanced Raman Spectroscopy
+- `public_health_infectious_disease_modeling`: Public Health - Infectious-disease Modeling
+- `earth_science_remote_sensing`: Earth Science - Remote Sensing
+
+## Data Schema
+
+Each record contains:
+
+- `subject`: human-readable subject name
+- `paper_id`: paper index within the subject subset
+- `paper_title`: canonical benchmark paper title
+- `question_id`: question identifier
+- `question`: canonical benchmark question wording
+- `choices`: answer choice dictionary keyed by `A`-`F`
+- `answer`: gold answer letter
+- `metadata`: extra metadata with:
+  - `Task-oriented Category`
+  - `question_key_term`
+  - `term_explanation`
+
+## Notes
+
+- The published dataset excludes source PDFs, markdown paper extracts, and internal evaluation outputs.
+- The benchmark uses canonical paper titles and normalized question wording across all five domains.
+- Data files are stored as JSON arrays and exposed as separate Hugging Face configs.
+
+## Citation
+
+If you publish this dataset, add the benchmark paper citation here.

data/earth_science_remote_sensing.json

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+[
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q1",
+    "question": "What is the number of land-cover / land-use classes classified in this study?",
+    "choices": {
+      "A": "3",
+      "B": "6",
+      "C": "9",
+      "D": "10",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "land cover",
+      "term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface representation is. Typical classes include urban, vegetation, water, bare land, etc."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q2",
+    "question": "What is the spatial extent of the study area?",
+    "choices": {
+      "A": "16,411 km²",
+      "B": "26,035 km²",
+      "C": "200,000 km²",
+      "D": "1,419,530 km²",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic area",
+      "term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sites to national or global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q3",
+    "question": "What is the geographic type of the study area?",
+    "choices": {
+      "A": "Urban",
+      "B": "Rural",
+      "C": "Mixed",
+      "D": "Natural (e.g., forest, wetland, desert)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic type",
+      "term_explanation": "The  geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context influences the spectral heterogeneity and classification difficulty."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q4",
+    "question": "What is the temporal scope of the data used?",
+    "choices": {
+      "A": "Single-scene imagery",
+      "B": "Short-term imagery ( ≤3 months)",
+      "C": "Mid-term imagery ( >3 and ≤12 months)",
+      "D": "Long-term imagery ( >1 year)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "time span",
+      "term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records (multi-year time series)."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q5",
+    "question": "What type of remote sensing data is used?",
+    "choices": {
+      "A": "Optical",
+      "B": "SAR",
+      "C": "LiDAR",
+      "D": "Multisource fusion",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "data type",
+      "term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spectral, spatial, and structural information available for classification."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q6",
+    "question": "Which specific satellite data is used?",
+    "choices": {
+      "A": "Sentinel-2",
+      "B": "Sentinel-1",
+      "C": "Luojia-1",
+      "D": "Sentinel-2 and Luojia-1",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "satellite",
+      "term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolution, revisit frequency, and sensor characteristics."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q7",
+    "question": "What is the spatial resolution of the primary imagery used?",
+    "choices": {
+      "A": "10 m",
+      "B": "16 m",
+      "C": "27 m",
+      "D": "1000 m",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "spatial resolution",
+      "term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, while moderate to coarse resolutions (e.g., 30 m or >100 m) are suited for regional to global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q8",
+    "question": "Are auxiliary features used beyond raw spectral bands?",
+    "choices": {
+      "A": "Vegetation indices (e.g., NDVI)",
+      "B": "Water features (e.g., NDWI)",
+      "C": "Vegetation indices and Water indices",
+      "D": "Elevation / DEM",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "features",
+      "term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other external variables. These features can significantly enhance classification performance."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q9",
+    "question": "What type of model is implemented in this study?",
+    "choices": {
+      "A": "SVM",
+      "B": "RF",
+      "C": "XGBoost",
+      "D": "CNN",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "ML model",
+      "term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-Net), or hybrid and transformer-based models. The model choice reflects the learning strategy and computational design."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q10",
+    "question": "What performance metrics are reported?",
+    "choices": {
+      "A": "Overall Accuracy (OA)",
+      "B": "F1-score",
+      "C": "Kappa",
+      "D": "OA and Kappa",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "performance metrics",
+      "term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics affects the interpretability and comparability of results."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q11",
+    "question": "Is any comparative analysis included?",
+    "choices": {
+      "A": "Compared with traditional classifiers (e.g., RF, SVM)",
+      "B": "Compared with deep models (e.g., U-Net variants)",
+      "C": "Ablation study",
+      "D": "No comparison reported",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "comparative analysis",
+      "term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into model robustness and improvement over existing methods."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "1",
+    "paper_title": "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method",
+    "question_id": "Q12",
+    "question": "What is the reported overall accuracy (OA)?",
+    "choices": {
+      "A": "69%",
+      "B": "74%",
+      "C": "77%",
+      "D": "98%",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "overall accuracy",
+      "term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measure of model performance."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q1",
+    "question": "What is the number of land-cover / land-use classes classified in this study?",
+    "choices": {
+      "A": "5",
+      "B": "12",
+      "C": "21",
+      "D": "37",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "land cover",
+      "term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface representation is. Typical classes include urban, vegetation, water, bare land, etc."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q2",
+    "question": "What is the spatial extent of the study area?",
+    "choices": {
+      "A": "7,317 km²",
+      "B": "41,576 km²",
+      "C": "67,558 km²",
+      "D": "166,338 km²",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic area",
+      "term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sites to national or global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q3",
+    "question": "What is the geographic type of the study area?",
+    "choices": {
+      "A": "Urban",
+      "B": "Rural",
+      "C": "Mixed",
+      "D": "Natural (e.g., forest, wetland, desert)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic type",
+      "term_explanation": "The  geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context influences the spectral heterogeneity and classification difficulty."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q4",
+    "question": "What is the temporal scope of the data used?",
+    "choices": {
+      "A": "Single-scene imagery",
+      "B": "Short-term imagery ( ≤3 months)",
+      "C": "Mid-term imagery ( >3 and ≤12 months)",
+      "D": "Long-term imagery ( >1 year)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "time span",
+      "term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records (multi-year time series)."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q5",
+    "question": "What type of remote sensing data is used?",
+    "choices": {
+      "A": "Optical",
+      "B": "SAR",
+      "C": "LiDAR",
+      "D": "Multisource fusion",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "data type",
+      "term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spectral, spatial, and structural information available for classification."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q6",
+    "question": "Which specific satellite data is used?",
+    "choices": {
+      "A": "Sentinel-1",
+      "B": "Sentinel-2",
+      "C": "Luojia-1",
+      "D": "Sentinel-2 and Luojia-1",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "satellite",
+      "term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolution, revisit frequency, and sensor characteristics."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q7",
+    "question": "What is the spatial resolution of the primary imagery used?",
+    "choices": {
+      "A": "2 m",
+      "B": "10 m",
+      "C": "21 m",
+      "D": "27 m",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "spatial resolution",
+      "term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, while moderate to coarse resolutions (e.g., 30 m or >100 m) are suited for regional to global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q8",
+    "question": "Are auxiliary features used beyond raw spectral bands?",
+    "choices": {
+      "A": "Vegetation indices (e.g., NDVI)",
+      "B": "Water features (e.g., NDWI)",
+      "C": "Vegetation indices and Water indices",
+      "D": "Elevation / DEM",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "features",
+      "term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other external variables. These features can significantly enhance classification performance."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q9",
+    "question": "What type of model is implemented in this study?",
+    "choices": {
+      "A": "SVM",
+      "B": "RF",
+      "C": "XGBoost",
+      "D": "CNN",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "ML model",
+      "term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-Net), or hybrid and transformer-based models. The model choice reflects the learning strategy and computational design."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q10",
+    "question": "What performance metrics are reported?",
+    "choices": {
+      "A": "Overall Accuracy (OA)",
+      "B": "F1-score",
+      "C": "Kappa",
+      "D": "IoU / mIoU",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "performance metrics",
+      "term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics affects the interpretability and comparability of results."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q11",
+    "question": "Is any comparative analysis included?",
+    "choices": {
+      "A": "Compared with traditional classifiers (e.g., RF, SVM)",
+      "B": "Compared with deep models (e.g., U-Net variants)",
+      "C": "Ablation study",
+      "D": "No comparison reported",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "comparative analysis",
+      "term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into model robustness and improvement over existing methods."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "2",
+    "paper_title": "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018",
+    "question_id": "Q12",
+    "question": "What is the reported overall accuracy (OA)?",
+    "choices": {
+      "A": "40.6%",
+      "B": "57.5%",
+      "C": "61.2%",
+      "D": "64.1%",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "overall accuracy",
+      "term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measure of model performance."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q1",
+    "question": "What is the number of land-cover / land-use classes classified in this study?",
+    "choices": {
+      "A": "2",
+      "B": "3",
+      "C": "9",
+      "D": "17",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "land cover",
+      "term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface representation is. Typical classes include urban, vegetation, water, bare land, etc."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q2",
+    "question": "What is the spatial extent of the study area?",
+    "choices": {
+      "A": "6,229 km²",
+      "B": "100,000 km²",
+      "C": "250,000 km²",
+      "D": "656,889 km²",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic area",
+      "term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sites to national or global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q3",
+    "question": "What is the geographic type of the study area?",
+    "choices": {
+      "A": "Urban",
+      "B": "Rural",
+      "C": "Mixed",
+      "D": "Natural (e.g., forest, wetland, desert)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic type",
+      "term_explanation": "The  geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context influences the spectral heterogeneity and classification difficulty."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q4",
+    "question": "What is the temporal scope of the data used?",
+    "choices": {
+      "A": "Single-scene imagery",
+      "B": "Short-term imagery ( ≤3 months)",
+      "C": "Mid-term imagery ( >3 and ≤12 months)",
+      "D": "Long-term imagery ( >1 year)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "time span",
+      "term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records (multi-year time series)."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q5",
+    "question": "What type of remote sensing data is used?",
+    "choices": {
+      "A": "Optical",
+      "B": "SAR",
+      "C": "LiDAR",
+      "D": "Multisource fusion",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "data type",
+      "term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spectral, spatial, and structural information available for classification."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q6",
+    "question": "Which specific satellite data is used?",
+    "choices": {
+      "A": "Sentinel-1",
+      "B": "Landsat series",
+      "C": "Sentinel-2",
+      "D": "PlanetScope",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "satellite",
+      "term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolution, revisit frequency, and sensor characteristics."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q7",
+    "question": "What is the spatial resolution of the primary imagery used?",
+    "choices": {
+      "A": "10 m",
+      "B": "18 m",
+      "C": "30 m",
+      "D": "60 m",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "spatial resolution",
+      "term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, while moderate to coarse resolutions (e.g., 30 m or >100 m) are suited for regional to global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q8",
+    "question": "Are auxiliary features used beyond raw spectral bands?",
+    "choices": {
+      "A": "Vegetation indices (e.g., EVI)",
+      "B": "Water features (e.g., NDWI)",
+      "C": "Vegetation indices and Water indices",
+      "D": "Elevation / DEM",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "features",
+      "term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other external variables. These features can significantly enhance classification performance."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q9",
+    "question": "What type of model is implemented in this study?",
+    "choices": {
+      "A": "SVM",
+      "B": "RF",
+      "C": "J4.8 Classifier",
+      "D": "MLC",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "ML model",
+      "term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-Net), or hybrid and transformer-based models. The model choice reflects the learning strategy and computational design."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q10",
+    "question": "What performance metrics are reported?",
+    "choices": {
+      "A": "Overall Accuracy (OA)",
+      "B": "F1-score",
+      "C": "Kappa",
+      "D": "OA and Kappa",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "performance metrics",
+      "term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics affects the interpretability and comparability of results."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q11",
+    "question": "Is any comparative analysis included?",
+    "choices": {
+      "A": "Compared with traditional classifiers (e.g., RF, SVM)",
+      "B": "Compared with deep models (e.g., U-Net variants)",
+      "C": "Ablation study",
+      "D": "No comparison reported",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "comparative analysis",
+      "term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into model robustness and improvement over existing methods."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "3",
+    "paper_title": "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data",
+    "question_id": "Q12",
+    "question": "What is the reported overall accuracy (OA)?",
+    "choices": {
+      "A": "53.88%",
+      "B": "57.88%",
+      "C": "59.83%",
+      "D": "64.89%",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "overall accuracy",
+      "term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measure of model performance."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q1",
+    "question": "What is the number of land-cover / land-use classes classified in this study?",
+    "choices": {
+      "A": "1",
+      "B": "7",
+      "C": "11",
+      "D": "20",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "land cover",
+      "term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface representation is. Typical classes include urban, vegetation, water, bare land, etc."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q2",
+    "question": "What is the spatial extent of the study area?",
+    "choices": {
+      "A": "67,000 km²",
+      "B": "132,000 km²",
+      "C": "151,942 km²",
+      "D": "315,000 km²",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic area",
+      "term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sites to national or global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q3",
+    "question": "What is the geographic type of the study area?",
+    "choices": {
+      "A": "Urban",
+      "B": "Rural",
+      "C": "Mixed",
+      "D": "Natural (e.g., forest, wetland, desert)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic type",
+      "term_explanation": "The  geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context influences the spectral heterogeneity and classification difficulty."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q4",
+    "question": "What is the temporal scope of the data used?",
+    "choices": {
+      "A": "Single-scene imagery",
+      "B": "Short-term imagery ( ≤3 months)",
+      "C": "Mid-term imagery ( >3 and ≤12 months)",
+      "D": "Long-term imagery ( >1 year)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "time span",
+      "term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records (multi-year time series)."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q5",
+    "question": "What type of remote sensing data is used?",
+    "choices": {
+      "A": "Optical",
+      "B": "SAR",
+      "C": "LiDAR",
+      "D": "Multisource fusion",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "data type",
+      "term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spectral, spatial, and structural information available for classification."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q6",
+    "question": "Which specific satellite data is used?",
+    "choices": {
+      "A": "Sentinel-1",
+      "B": "Sentinel-2",
+      "C": "Luojia-1",
+      "D": "Multisources",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "satellite",
+      "term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolution, revisit frequency, and sensor characteristics."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q7",
+    "question": "What is the spatial resolution of the primary imagery used?",
+    "choices": {
+      "A": "5 m",
+      "B": "10 m",
+      "C": "30 m",
+      "D": "5 km",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "spatial resolution",
+      "term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, while moderate to coarse resolutions (e.g., 30 m or >100 m) are suited for regional to global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q8",
+    "question": "Are auxiliary features used beyond raw spectral bands?",
+    "choices": {
+      "A": "Vegetation indices only (e.g., NDVI, LAI, FAPAR)",
+      "B": "Vegetation + energy fluxes (e.g., ET, GPP)",
+      "C": "Vegetation + albedo/emissivity (e.g., BBE, white-sky albedo)",
+      "D": "Albedo/emissivity",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "features",
+      "term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other external variables. These features can significantly enhance classification performance."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q9",
+    "question": "What type of model is implemented in this study?",
+    "choices": {
+      "A": "SVM",
+      "B": "RF",
+      "C": "XGBoost",
+      "D": "CNN",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "ML model",
+      "term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-Net), or hybrid and transformer-based models. The model choice reflects the learning strategy and computational design."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q10",
+    "question": "What performance metrics are reported?",
+    "choices": {
+      "A": "Overall Accuracy (OA)",
+      "B": "F1-score",
+      "C": "Kappa",
+      "D": "OA and Kappa",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "performance metrics",
+      "term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics affects the interpretability and comparability of results."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q11",
+    "question": "Is any comparative analysis included?",
+    "choices": {
+      "A": "Compared with traditional classifiers (e.g., RF, SVM)",
+      "B": "Compared with deep models (e.g., U-Net variants)",
+      "C": "Compared with previous products",
+      "D": "No comparison reported",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "comparative analysis",
+      "term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into model robustness and improvement over existing methods."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "4",
+    "paper_title": "Annual dynamics of global land cover and its long-term changes from 1982 to 2015",
+    "question_id": "Q12",
+    "question": "What is the reported overall accuracy (OA)?",
+    "choices": {
+      "A": "73.54%",
+      "B": "86.51%",
+      "C": "87.12%",
+      "D": "92.26%",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "overall accuracy",
+      "term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measure of model performance."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q1",
+    "question": "What is the number of land-cover / land-use classes classified in this study?",
+    "choices": {
+      "A": "1",
+      "B": "3",
+      "C": "34",
+      "D": "155",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "land cover",
+      "term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface representation is. Typical classes include urban, vegetation, water, bare land, etc."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q2",
+    "question": "What is the spatial extent of the study area?",
+    "choices": {
+      "A": "108,962 km²",
+      "B": "340,625 km²",
+      "C": "218,859 km²",
+      "D": "797,076 km²",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic area",
+      "term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sites to national or global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q3",
+    "question": "What is the geographic type of the study area?",
+    "choices": {
+      "A": "Urban",
+      "B": "Rural",
+      "C": "Mixed",
+      "D": "Natural (e.g., forest, wetland, desert)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "geographic type",
+      "term_explanation": "The  geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context influences the spectral heterogeneity and classification difficulty."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q4",
+    "question": "What is the temporal scope of the data used?",
+    "choices": {
+      "A": "Single-scene imagery",
+      "B": "Short-term imagery ( ≤3 months)",
+      "C": "Mid-term imagery ( >3 and ≤12 months)",
+      "D": "Long-term imagery ( >1 year)",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "time span",
+      "term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records (multi-year time series)."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q5",
+    "question": "What type of remote sensing data is used?",
+    "choices": {
+      "A": "Optical",
+      "B": "SAR",
+      "C": "LiDAR",
+      "D": "Multisource fusion",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "data type",
+      "term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spectral, spatial, and structural information available for classification."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q6",
+    "question": "Which specific satellite data is used?",
+    "choices": {
+      "A": "Sentinel-1",
+      "B": "Landsat series",
+      "C": "VIIRS NTL",
+      "D": "Landsat series, Sentinel-1 and VIIRS NTL",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "satellite",
+      "term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolution, revisit frequency, and sensor characteristics."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q7",
+    "question": "What is the spatial resolution of the primary imagery used?",
+    "choices": {
+      "A": "10 m",
+      "B": "30 m",
+      "C": "100 m",
+      "D": "250 m",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "spatial resolution",
+      "term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, while moderate to coarse resolutions (e.g., 30 m or >100 m) are suited for regional to global scales."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q8",
+    "question": "Are auxiliary features used beyond raw spectral bands?",
+    "choices": {
+      "A": "Vegetation indices (e.g., EVI)",
+      "B": "Vegetation + energy fluxes (e.g., ET, GPP)",
+      "C": "Water features (e.g., NDWI, MNDWI)",
+      "D": "Vegetation indices and Water indices",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "features",
+      "term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other external variables. These features can significantly enhance classification performance."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q9",
+    "question": "What type of model is implemented in this study?",
+    "choices": {
+      "A": "Spatially Explicit",
+      "B": "Temporal Consistency",
+      "C": "Spatially Explicit and Temporal Consistency",
+      "D": "Transformer",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "ML model",
+      "term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-Net), or hybrid and transformer-based models. The model choice reflects the learning strategy and computational design."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q10",
+    "question": "What performance metrics are reported?",
+    "choices": {
+      "A": "Overall Accuracy (OA)",
+      "B": "F1-score",
+      "C": "Kappa",
+      "D": "OA and Kappa",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "performance metrics",
+      "term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics affects the interpretability and comparability of results."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q11",
+    "question": "Is any comparative analysis included?",
+    "choices": {
+      "A": "Compared with traditional classifiers (e.g., RF, SVM)",
+      "B": "Compared with deep models (e.g., U-Net variants)",
+      "C": "Compared with previous products",
+      "D": "No comparison reported",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "comparative analysis",
+      "term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into model robustness and improvement over existing methods."
+    }
+  },
+  {
+    "subject": "Earth Science - Remote Sensing",
+    "paper_id": "5",
+    "paper_title": "Annual maps of global artificial impervious area (GAIA) between 1985 and 2018",
+    "question_id": "Q12",
+    "question": "What is the reported overall accuracy (OA)?",
+    "choices": {
+      "A": "15%",
+      "B": "43%",
+      "C": "70%",
+      "D": "89%",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "overall accuracy",
+      "term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measure of model performance."
+    }
+  }
+]

data/engineering_human_factor.json

@@ -0,0 +1,1367 @@
+[
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q1",
+    "question": "What are the subjects’ occupational roles?",
+    "choices": {
+      "A": "Drivers",
+      "B": "Warehouse worker",
+      "C": "Electrical line workers",
+      "D": "Atheletes",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Occupation",
+      "term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q2",
+    "question": "What specific task or activity are the subjects performing?",
+    "choices": {
+      "A": "Hoisting",
+      "B": "Lifting",
+      "C": "Standing",
+      "D": "Electrical panel work",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Primary Task",
+      "term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q4",
+    "question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
+    "choices": {
+      "A": "Lab",
+      "B": "Field",
+      "C": "Computer simulated",
+      "D": "Mixed",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Study Environment",
+      "term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q5",
+    "question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
+    "choices": {
+      "A": "Empatica E4 wristband",
+      "B": "EMG",
+      "C": "ECG",
+      "D": "EEG",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q6",
+    "question": "What is the anatomical or body placement of the sensors used in the study?",
+    "choices": {
+      "A": "Wrist",
+      "B": "Waist",
+      "C": "Chest",
+      "D": "Ankle",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor placement",
+      "term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q7",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "0-20 Hz",
+      "B": "20-40 Hz",
+      "C": "40-60 Hz",
+      "D": "Above 60 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q8",
+    "question": "What is the total number of participants involved in the study?",
+    "choices": {
+      "A": "0-10",
+      "B": "10-20",
+      "C": "20-30",
+      "D": "> 30",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sample Size",
+      "term_explanation": "How many people took part in the study?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q9",
+    "question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
+    "choices": {
+      "A": "Borg RPE 6-20",
+      "B": "Borg RPE CR 10",
+      "C": "PVT, RULA",
+      "D": "Strength",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Label",
+      "term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q10",
+    "question": "What is the primary modeling objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q11",
+    "question": "What is the data partitioning strategy used during model training, and what are the parameters?",
+    "choices": {
+      "A": "T-T split; 0.8, 0.2",
+      "B": "T-D-T split; 0.525, 0.175, 0.3",
+      "C": "K-fold; 7 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q12",
+    "question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
+    "choices": {
+      "A": "5",
+      "B": "256",
+      "C": "1000",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "How many times does the model go through all the data while learning from it?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q13",
+    "question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "Precision",
+      "C": "Recall",
+      "D": "F1 score",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "What measures or scores do the researchers use to see how well the  model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "1",
+    "paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
+    "question_id": "Q14",
+    "question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Accuracy 99.7 %",
+      "B": ">60 % min accuracy",
+      "C": "p < 0.003 for any classical method compared to our method",
+      "D": "89.5±2.5 % F1-score",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the final results or scores that show how well the computer model performed?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q1",
+    "question": "What are the subjects’ occupational roles?",
+    "choices": {
+      "A": "Drivers",
+      "B": "Warehouse worker",
+      "C": "Electric linemen",
+      "D": "Civil aviator",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Occupation",
+      "term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q2",
+    "question": "What specific task or activity are the subjects performing?",
+    "choices": {
+      "A": "Communication",
+      "B": "Tracking",
+      "C": "System monitoring",
+      "D": "Resource management",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Primary Task",
+      "term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q4",
+    "question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
+    "choices": {
+      "A": "Lab",
+      "B": "Field",
+      "C": "Computer simulated",
+      "D": "Mixed",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Study Environment",
+      "term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q5",
+    "question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
+    "choices": {
+      "A": "IMU",
+      "B": "EMG",
+      "C": "ECG",
+      "D": "EEG",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q6",
+    "question": "What is the anatomical or body placement of the sensors used in the study?",
+    "choices": {
+      "A": "Wrist",
+      "B": "Waist",
+      "C": "Head",
+      "D": "Ankle",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor placement",
+      "term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q7",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "0-20 Hz",
+      "B": "20-40 Hz",
+      "C": "40-60 Hz",
+      "D": "Above 60 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q8",
+    "question": "What is the total number of participants involved in the study?",
+    "choices": {
+      "A": "0-10",
+      "B": "10-20",
+      "C": "20-30",
+      "D": "> 30",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sample Size",
+      "term_explanation": "How many people took part in the study?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q9",
+    "question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
+    "choices": {
+      "A": "Borg RPE 6-20",
+      "B": "Borg RPE CR 10",
+      "C": "PVT, RULA",
+      "D": "Stanford Sleepiness Scale",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Label",
+      "term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q10",
+    "question": "What is the primary modeling objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q11",
+    "question": "What is the data partitioning strategy used during model training, and what are the parameters?",
+    "choices": {
+      "A": "T-T split; 0.8, 0.2",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 7 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q12",
+    "question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
+    "choices": {
+      "A": "50",
+      "B": "300",
+      "C": "1000",
+      "D": "3500",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "How many times does the model go through all the data while learning from it?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q13",
+    "question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "Precision",
+      "C": "Recall",
+      "D": "F1 score",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "What measures or scores do the researchers use to see how well the  model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "2",
+    "paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
+    "question_id": "Q14",
+    "question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Detection accuracy of 99.7 %",
+      "B": "98 % accuracy, >97 % precision, >97 % recall and >98 % F1 score",
+      "C": "p < 0.003",
+      "D": "A strong correlation",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the final results or scores that show how well the computer model performed?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q1",
+    "question": "What are the subjects’ occupational roles?",
+    "choices": {
+      "A": "Drivers",
+      "B": "Warehouse worker",
+      "C": "Electric linemen",
+      "D": "Atheletes",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Occupation",
+      "term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q2",
+    "question": "What specific task or activity are the subjects performing?",
+    "choices": {
+      "A": "Driving",
+      "B": "Lifting",
+      "C": "Standing",
+      "D": "Panel work",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Primary Task",
+      "term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q4",
+    "question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
+    "choices": {
+      "A": "Lab",
+      "B": "Field",
+      "C": "Computer simulated",
+      "D": "Mixed",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Study Environment",
+      "term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q5",
+    "question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
+    "choices": {
+      "A": "IMU",
+      "B": "EEG",
+      "C": "ECG",
+      "D": "IMU and EEG",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q6",
+    "question": "What is the anatomical or body placement of the sensors used in the study?",
+    "choices": {
+      "A": "Head",
+      "B": "Neck",
+      "C": "Sternum",
+      "D": "Head, neck and sternum",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor placement",
+      "term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q7",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "0-20 Hz",
+      "B": "20-40 Hz",
+      "C": "40-60 Hz",
+      "D": "Above 60 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q8",
+    "question": "What is the total number of participants involved in the study?",
+    "choices": {
+      "A": "0-10",
+      "B": "10-20",
+      "C": "20-30",
+      "D": "> 30",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sample Size",
+      "term_explanation": "How many people took part in the study?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q9",
+    "question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
+    "choices": {
+      "A": "Borg RPE 6-20",
+      "B": "Borg RPE CR 10",
+      "C": "PVT, RULA",
+      "D": "Strength",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Label",
+      "term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q10",
+    "question": "What is the primary modeling objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Clustering and classification",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q11",
+    "question": "What is the data partitioning strategy used during model training, and what are the parameters?",
+    "choices": {
+      "A": "T-T split; 20, 2",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q12",
+    "question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
+    "choices": {
+      "A": "50",
+      "B": "300",
+      "C": "1000",
+      "D": "3500",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "How many times does the model go through all the data while learning from it?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q13",
+    "question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "Precision",
+      "C": "Sensitivity",
+      "D": "F1 score",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "What measures or scores do the researchers use to see how well the  model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "3",
+    "paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
+    "question_id": "Q14",
+    "question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Detection accuracy of 99.7 %",
+      "B": "98 % accuracy, >97 % precision, >97 % recall and >98 % F1 score",
+      "C": ">79 % precision, >70 % sensitivity and >74 % F1 score",
+      "D": "A strong correlation",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the final results or scores that show how well the computer model performed?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q1",
+    "question": "What are the subjects’ occupational roles?",
+    "choices": {
+      "A": "Drivers",
+      "B": "Warehouse worker",
+      "C": "Electric linemen",
+      "D": "Atheletes",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Occupation",
+      "term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q2",
+    "question": "What specific task or activity are the subjects performing?",
+    "choices": {
+      "A": "Assembly",
+      "B": "Lifting",
+      "C": "Walking with load",
+      "D": "Walking without load",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Primary Task",
+      "term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q4",
+    "question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
+    "choices": {
+      "A": "Lab",
+      "B": "Field",
+      "C": "Computer simulated",
+      "D": "Reference or open data",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Study Environment",
+      "term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q5",
+    "question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
+    "choices": {
+      "A": "GSR",
+      "B": "HR",
+      "C": "EMG",
+      "D": "Infrared camera",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q6",
+    "question": "What is the anatomical or body placement of the sensors used in the study?",
+    "choices": {
+      "A": "Wrist",
+      "B": "Waist",
+      "C": "Head",
+      "D": "Ankle",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor placement",
+      "term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q7",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "5 Hz",
+      "B": "10 Hz",
+      "C": "15 Hz",
+      "D": "20 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q8",
+    "question": "What is the total number of participants involved in the study?",
+    "choices": {
+      "A": "10, 15",
+      "B": "20, 10",
+      "C": "30, 15",
+      "D": "24, 11",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sample Size",
+      "term_explanation": "How many people took part in the study?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q9",
+    "question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
+    "choices": {
+      "A": "Borg RPE 6-20",
+      "B": "Borg RPE CR 10",
+      "C": "PVT, RULA",
+      "D": "Strength",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Label",
+      "term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q10",
+    "question": "What is the primary modeling objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q11",
+    "question": "What is the data partitioning strategy used during model training, and what are the parameters?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-T split; 0.8, 0.2",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q12",
+    "question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
+    "choices": {
+      "A": "50",
+      "B": "100",
+      "C": "600",
+      "D": "1700",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "How many times does the model go through all the data while learning from it?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q13",
+    "question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "Precision",
+      "C": "Recall",
+      "D": "Predicted, recall and F1-score",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "What measures or scores do the researchers use to see how well the  model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "4",
+    "paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
+    "question_id": "Q14",
+    "question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Detection accuracy of 99.7 %",
+      "B": "98 % accuracy, >97 % precision, >97 % recall and >98 % F1 score",
+      "C": ">70 % precision, >75 % recall and >72 % F1 score",
+      "D": "A strong correlation",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the final results or scores that show how well the computer model performed?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q1",
+    "question": "What are the subjects’ occupational roles?",
+    "choices": {
+      "A": "Drivers",
+      "B": "Warehouse worker",
+      "C": "Electric linemen",
+      "D": "Atheletes",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Occupation",
+      "term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q2",
+    "question": "What specific task or activity are the subjects performing?",
+    "choices": {
+      "A": "Walking",
+      "B": "Bending",
+      "C": "Standing",
+      "D": "Assembly",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Primary Task",
+      "term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q4",
+    "question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
+    "choices": {
+      "A": "Lab",
+      "B": "Field",
+      "C": "Computer simulated",
+      "D": "Reference or open data",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Study Environment",
+      "term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q5",
+    "question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
+    "choices": {
+      "A": "IMU",
+      "B": "EMG",
+      "C": "ECG",
+      "D": "EEG",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q6",
+    "question": "What is the anatomical or body placement of the sensors used in the study?",
+    "choices": {
+      "A": "Wrist",
+      "B": "Torso",
+      "C": "Hip",
+      "D": "Ankle",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor placement",
+      "term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q7",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "0-20 Hz",
+      "B": "20-40 Hz",
+      "C": "40-60 Hz",
+      "D": "Above 60 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q8",
+    "question": "What is the total number of participants involved in the study?",
+    "choices": {
+      "A": "0-10",
+      "B": "10-20",
+      "C": "20-30",
+      "D": "> 30",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sample Size",
+      "term_explanation": "How many people took part in the study?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q9",
+    "question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
+    "choices": {
+      "A": "Borg RPE",
+      "B": "Stanford Sleepiness Scale",
+      "C": "PVT, RULA",
+      "D": "Strength",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Label",
+      "term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q10",
+    "question": "What is the primary modeling objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Classification",
+      "C": "Dimension reduction",
+      "D": "Classification and regression",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q11",
+    "question": "What is the data partitioning strategy used during model training, and what are the parameters?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-T split; 0.8, 0.2",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q12",
+    "question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
+    "choices": {
+      "A": "100",
+      "B": "200",
+      "C": "300",
+      "D": "500 and 1000",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "How many times does the model go through all the data while learning from it?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q13",
+    "question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "R squre",
+      "C": "Recall",
+      "D": "MAE",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "What measures or scores do the researchers use to see how well the  model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
+    }
+  },
+  {
+    "subject": "Engineering - Human Factor",
+    "paper_id": "5",
+    "paper_title": "Worker’s physical fatigue classification using neural networks",
+    "question_id": "Q14",
+    "question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Acc, 0.999; R square 0.98",
+      "B": ">80 % accuracy",
+      "C": "R square 0.98",
+      "D": "R square 0.993",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the final results or scores that show how well the computer model performed?"
+    }
+  }
+]

data/material_science_additive_manufacturing.json

@@ -0,0 +1,1472 @@
+[
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q1",
+    "question": "What type of additive manufacturing process is studied?",
+    "choices": {
+      "A": "LPBF",
+      "B": "Inkjet printing",
+      "C": "Aerojet printing",
+      "D": "Direct ink writing",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "AM process",
+      "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q2",
+    "question": "What type of material is used for printing?",
+    "choices": {
+      "A": "Ti64",
+      "B": "Water",
+      "C": "Silver",
+      "D": "Glycerol",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Material type",
+      "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q3",
+    "question": "What kind of shape or product is printed?",
+    "choices": {
+      "A": "Thin wall",
+      "B": "Droplet",
+      "C": "Single layer",
+      "D": "Line",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Part and design",
+      "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q4",
+    "question": "What is the primary defect being studied?",
+    "choices": {
+      "A": "Porosity",
+      "B": "Crack",
+      "C": "Satelitte",
+      "D": "Overspray",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Defect",
+      "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q5",
+    "question": "What sensors are used to measure the process?",
+    "choices": {
+      "A": "CCD camera",
+      "B": "Strobing camera",
+      "C": "Thermocouple",
+      "D": "Acoustic",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q6",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "0-20 Hz",
+      "B": "20-40 Hz",
+      "C": "40-60 Hz",
+      "D": "Above 60 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q7",
+    "question": "If relevant, what is the spatial resolution (µm)?",
+    "choices": {
+      "A": "<50 µm",
+      "B": "50 -100 µm",
+      "C": "100 -150 µm",
+      "D": "Above 150 µm",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spatial resolution",
+      "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q8",
+    "question": "What is the machine learning objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q9",
+    "question": "What machine learning algorithm is used?",
+    "choices": {
+      "A": "PCA",
+      "B": "SVM",
+      "C": "CNN",
+      "D": "RF",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q10",
+    "question": "How are the data split during machine learning?",
+    "choices": {
+      "A": "T-T split; 0.8, 0.2",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q11",
+    "question": "How many replications are conducted during machine learning, if any?",
+    "choices": {
+      "A": "1",
+      "B": "5",
+      "C": "50",
+      "D": "100",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q12",
+    "question": "How many epochs are used during machine learning, if any?",
+    "choices": {
+      "A": "5",
+      "B": "256",
+      "C": "500",
+      "D": "1000",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "In training the model, how many times does it go through the whole set of data?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q13",
+    "question": "What metrics are used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "Precision",
+      "C": "Recall",
+      "D": "F1 score",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "1",
+    "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach",
+    "question_id": "Q14",
+    "question": "What are the values for these metrics?",
+    "choices": {
+      "A": "Accuracy > 90%",
+      "B": ">97.8 % accuracy, >98.2 % precision, >98.2 % recall and >98.2 % F1 score",
+      "C": "p < 0.003 for any classical method compared to our method",
+      "D": "89.5±2.5 % accuracy",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the actual results or scores from those metrics?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q1",
+    "question": "What type of additive manufacturing process is studied?",
+    "choices": {
+      "A": "LPBF",
+      "B": "FFF",
+      "C": "Aerojet printing",
+      "D": "Direct ink writing",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "AM process",
+      "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q2",
+    "question": "What type of material is used for printing?",
+    "choices": {
+      "A": "Ti64",
+      "B": "ABS",
+      "C": "Silver",
+      "D": "Glycerol",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Material type",
+      "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q3",
+    "question": "What kind of shape or product is printed?",
+    "choices": {
+      "A": "Thin wall",
+      "B": "Droplet",
+      "C": "Single layer",
+      "D": "Full part",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Part and design",
+      "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q4",
+    "question": "What is the primary defect being studied?",
+    "choices": {
+      "A": "Porosity",
+      "B": "Crack",
+      "C": "Satelitte",
+      "D": "Overspray",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Defect",
+      "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q5",
+    "question": "What sensors are used to measure the process?",
+    "choices": {
+      "A": "Thermal camera",
+      "B": "Strobing camera",
+      "C": "Thermocouple",
+      "D": "3D scanner",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q6",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "0-20 Hz",
+      "B": "20-40 Hz",
+      "C": "40-60 Hz",
+      "D": "Above 60 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q7",
+    "question": "If relevant, what is the spatial resolution (µm)?",
+    "choices": {
+      "A": "<50 µm",
+      "B": "50 -100 µm",
+      "C": "100 -150 µm",
+      "D": "Above 150 µm",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spatial resolution",
+      "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q8",
+    "question": "What is the machine learning objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q9",
+    "question": "What machine learning algorithm is used?",
+    "choices": {
+      "A": "Bagging",
+      "B": "Boosting",
+      "C": "RF",
+      "D": "SVM",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q10",
+    "question": "How are the data split during machine learning?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q11",
+    "question": "How many replications are conducted during machine learning, if any?",
+    "choices": {
+      "A": "5",
+      "B": "256",
+      "C": "1000",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q12",
+    "question": "How many epochs are used during machine learning, if any?",
+    "choices": {
+      "A": "50",
+      "B": "300",
+      "C": "1000",
+      "D": "3500",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "In training the model, how many times does it go through the whole set of data?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q13",
+    "question": "What metrics are used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "F-measure",
+      "C": "G-mean",
+      "D": "F-measure and G-mean",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "2",
+    "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models",
+    "question_id": "Q14",
+    "question": "What are the values for these metrics?",
+    "choices": {
+      "A": "Accuracy of 98 %",
+      "B": "98 % accuracy, >35 % F-measure, >46 % G-mean",
+      "C": "p < 0.003",
+      "D": "A strong correlation",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the actual results or scores from those metrics?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q1",
+    "question": "What type of additive manufacturing process is studied?",
+    "choices": {
+      "A": "LPBF",
+      "B": "Inkjet printing",
+      "C": "Aerojet printing",
+      "D": "Direct ink writing",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "AM process",
+      "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q2",
+    "question": "What type of material is used for printing?",
+    "choices": {
+      "A": "Ti64",
+      "B": "Water",
+      "C": "Silver",
+      "D": "Glycerol",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Material type",
+      "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q3",
+    "question": "What kind of shape or product is printed?",
+    "choices": {
+      "A": "Thin wall",
+      "B": "Droplet",
+      "C": "Single layer",
+      "D": "Full part",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Part and design",
+      "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q4",
+    "question": "What is the primary defect being studied?",
+    "choices": {
+      "A": "Porosity",
+      "B": "Crack",
+      "C": "Satelitte",
+      "D": "Overspray",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Defect",
+      "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q5",
+    "question": "What sensors are used to measure the process?",
+    "choices": {
+      "A": "Thermal camera",
+      "B": "Strobing camera",
+      "C": "Thermocouple",
+      "D": "Acoustic",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q6",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "0-20 Hz",
+      "B": "20-40 Hz",
+      "C": "40-60 Hz",
+      "D": "Above 60 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q7",
+    "question": "If relevant, what is the spatial resolution (µm)?",
+    "choices": {
+      "A": "<50 µm",
+      "B": "50 -100 µm",
+      "C": "100 -150 µm",
+      "D": "Above 150 µm",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spatial resolution",
+      "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q8",
+    "question": "What is the machine learning objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q9",
+    "question": "What machine learning algorithm is used?",
+    "choices": {
+      "A": "Bagging",
+      "B": "Boosting",
+      "C": "RF",
+      "D": "SVM",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q10",
+    "question": "How are the data split during machine learning?",
+    "choices": {
+      "A": "T-T split; 5/6, 1/6",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q11",
+    "question": "How many replications are conducted during machine learning, if any?",
+    "choices": {
+      "A": "5",
+      "B": "256",
+      "C": "500",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q12",
+    "question": "How many epochs are used during machine learning, if any?",
+    "choices": {
+      "A": "50",
+      "B": "300",
+      "C": "1000",
+      "D": "3500",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "In training the model, how many times does it go through the whole set of data?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q13",
+    "question": "What metrics are used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Precision and Recall",
+      "B": "Precision",
+      "C": "Recall",
+      "D": "F1 score",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "3",
+    "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing",
+    "question_id": "Q14",
+    "question": "What are the values for these metrics?",
+    "choices": {
+      "A": "Accuracy > 90%",
+      "B": ">95 % precision, >91 % recall and >93 % F1 score",
+      "C": "p < 0.003 for any classical method compared to our method",
+      "D": "89.5±2.5 % accuracy",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the actual results or scores from those metrics?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q1",
+    "question": "What type of additive manufacturing process is studied?",
+    "choices": {
+      "A": "LPBF",
+      "B": "Inkjet printing",
+      "C": "Aerojet printing",
+      "D": "Direct ink writing",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "AM process",
+      "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q2",
+    "question": "What type of material is used for printing?",
+    "choices": {
+      "A": "Ti64",
+      "B": "Water",
+      "C": "Silver",
+      "D": "Glycerol",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Material type",
+      "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q3",
+    "question": "What kind of shape or product is printed?",
+    "choices": {
+      "A": "Thin wall",
+      "B": "Droplet",
+      "C": "Single layer",
+      "D": "Full part",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Part and design",
+      "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q4",
+    "question": "What is the primary defect being studied?",
+    "choices": {
+      "A": "Porosity",
+      "B": "Crack",
+      "C": "Satelitte",
+      "D": "Overspray",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Defect",
+      "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q5",
+    "question": "What sensors are used to measure the process?",
+    "choices": {
+      "A": "Thermal camera",
+      "B": "Strobing camera",
+      "C": "Thermocouple",
+      "D": "Acoustic",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q6",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "0-20 Hz",
+      "B": "20-40 Hz",
+      "C": "40-60 Hz",
+      "D": "Above 60 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q7",
+    "question": "If relevant, what is the spatial resolution (µm)?",
+    "choices": {
+      "A": "<50 µm",
+      "B": "50 -100 µm",
+      "C": "100 -150 µm",
+      "D": "Above 150 µm",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spatial resolution",
+      "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q8",
+    "question": "What is the machine learning objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q9",
+    "question": "What machine learning algorithm is used?",
+    "choices": {
+      "A": "SVM",
+      "B": "CNN",
+      "C": "LR",
+      "D": "Bayesian online change detection",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q10",
+    "question": "How are the data split during machine learning?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q11",
+    "question": "How many replications are conducted during machine learning, if any?",
+    "choices": {
+      "A": "5",
+      "B": "256",
+      "C": "1000",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q12",
+    "question": "How many epochs are used during machine learning, if any?",
+    "choices": {
+      "A": "50",
+      "B": "170",
+      "C": "600",
+      "D": "1700",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "In training the model, how many times does it go through the whole set of data?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q13",
+    "question": "What metrics are used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Precision and Recall",
+      "B": "Precision",
+      "C": "Recall",
+      "D": "F1 score",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "4",
+    "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing",
+    "question_id": "Q14",
+    "question": "What are the values for these metrics?",
+    "choices": {
+      "A": "Accuracy > 90%",
+      "B": ">80 % precision, >70 % recall and >75 % F1 score",
+      "C": "p < 0.003 for any classical method compared to our method",
+      "D": "89.5±2.5 % accuracy",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the actual results or scores from those metrics?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q1",
+    "question": "What type of additive manufacturing process is studied?",
+    "choices": {
+      "A": "LPBF",
+      "B": "DED",
+      "C": "Aerojet printing",
+      "D": "LPBF and DED",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "AM process",
+      "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q2",
+    "question": "What type of material is used for printing?",
+    "choices": {
+      "A": "Ti64",
+      "B": "Inconel 625",
+      "C": "Silver",
+      "D": "Ti64 and Inconel 625",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Material type",
+      "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q3",
+    "question": "What kind of shape or product is printed?",
+    "choices": {
+      "A": "Thin wall",
+      "B": "Overhang part",
+      "C": "Single layer",
+      "D": "Overhang part and thin wall",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Part and design",
+      "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q4",
+    "question": "What is the primary defect being studied?",
+    "choices": {
+      "A": "Porosity",
+      "B": "Crack",
+      "C": "Satelitte",
+      "D": "Overspray",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Defect",
+      "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q5",
+    "question": "What sensors are used to measure the process?",
+    "choices": {
+      "A": "Thermal camera",
+      "B": "Photodetector",
+      "C": "Thermal camera and photodetector",
+      "D": "Acoustic",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sensor type",
+      "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q6",
+    "question": "What is the sampling rate (Hz)?",
+    "choices": {
+      "A": "0-20 Hz",
+      "B": "20-40 Hz",
+      "C": "40-60 Hz",
+      "D": "Above 60 Hz",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Sampling rate",
+      "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q7",
+    "question": "If relevant, what is the spatial resolution (µm)?",
+    "choices": {
+      "A": "<50 µm",
+      "B": "50 -100 µm",
+      "C": "100 -150 µm",
+      "D": "Above 150 µm",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spatial resolution",
+      "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q8",
+    "question": "What is the machine learning objective in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Classification",
+      "C": "Dimension reduction",
+      "D": "Classification and regression",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q9",
+    "question": "What machine learning algorithm is used?",
+    "choices": {
+      "A": "SVM",
+      "B": "CNN",
+      "C": "LR",
+      "D": "Regression",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q10",
+    "question": "How are the data split during machine learning?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q11",
+    "question": "How many replications are conducted during machine learning, if any?",
+    "choices": {
+      "A": "10",
+      "B": "256",
+      "C": "1000",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q12",
+    "question": "How many epochs are used during machine learning, if any?",
+    "choices": {
+      "A": "100",
+      "B": "200",
+      "C": "300",
+      "D": "4700",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "In training the model, how many times does it go through the whole set of data?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q13",
+    "question": "What metrics are used to evaluate the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "F score",
+      "C": "Recall",
+      "D": "MAE",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?"
+    }
+  },
+  {
+    "subject": "Material Science - Additive Manufacturing",
+    "paper_id": "5",
+    "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults",
+    "question_id": "Q14",
+    "question": "What are the values for these metrics?",
+    "choices": {
+      "A": "Acc, 0.999; R square 0.98",
+      "B": "Acc, 0.999",
+      "C": "R square 0.98",
+      "D": "F score >0.9",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "What were the actual results or scores from those metrics?"
+    }
+  }
+]

data/physics_surface_enhanced_raman_spectroscopy.json

@@ -0,0 +1,1367 @@
+[
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q1",
+    "question": "What are the main analytes type studied?",
+    "choices": {
+      "A": "Soft tissue",
+      "B": "Oral disease",
+      "C": "Todd Hewitt Broth",
+      "D": "Periodontal pathogens",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Analytes",
+      "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q2",
+    "question": "What are the material and structure, or morphology of the SERS substrates used?",
+    "choices": {
+      "A": "Gold",
+      "B": "Sliver",
+      "C": "Titanium layer",
+      "D": "AFM",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "SERS Substrates",
+      "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q3",
+    "question": "How many analytes are investigated?",
+    "choices": {
+      "A": "2",
+      "B": "3",
+      "C": "4",
+      "D": "5",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Number of Analytes",
+      "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q4",
+    "question": "What is the excitation laser wavelength used for SERS measurements?",
+    "choices": {
+      "A": "90 mW",
+      "B": "633 nm",
+      "C": "785 nm",
+      "D": "30 s",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Laser wavelength",
+      "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q5",
+    "question": "What is the spectral range collected for the analysis of the analytes?",
+    "choices": {
+      "A": "301 cm⁻¹ to 2000 cm⁻¹",
+      "B": "300 cm⁻¹ to 2001 cm⁻¹",
+      "C": "400 cm⁻¹ to 4000 cm⁻¹",
+      "D": "200 cm⁻¹ to 2000 cm⁻¹",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spectral Range",
+      "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q6",
+    "question": "How many spectra are collected per analyte under each experimental condition?",
+    "choices": {
+      "A": "100",
+      "B": "80",
+      "C": "10",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Spectra for Each Analyte Under Each Condition",
+      "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q7",
+    "question": "What is the primary machine learning task addressed in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)"
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q8",
+    "question": "Which machine learning algorithm is implemented?",
+    "choices": {
+      "A": "PCA",
+      "B": "SVM",
+      "C": "KNN",
+      "D": "RF",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q9",
+    "question": "What data splitting strategy is applied, and the parameters?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-D-T split; 0.525, 0.175, 0.3",
+      "C": "K-fold; 7 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q10",
+    "question": "How many experimental replications are conducted to ensure reproducibility?",
+    "choices": {
+      "A": "5",
+      "B": "256",
+      "C": "1000",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q11",
+    "question": "How many epochs are used during model training?",
+    "choices": {
+      "A": "5",
+      "B": "256",
+      "C": "1000",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q12",
+    "question": "What performance metrics are employed to evaluate the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "Precision",
+      "C": "Recall",
+      "D": "F1 score",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "1",
+    "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy",
+    "question_id": "Q13",
+    "question": "What are the reported performance values?",
+    "choices": {
+      "A": "Detection accuracy of 99.7 % ± 0.5 % for Aa, 99.2 % ± 0.7 % for Pg, and 97.8 % ± 0.9 % for Sm",
+      "B": ">97.8 % accuracy, >98.2 % precision, >98.2 % recall and >98.2 % F1 score",
+      "C": "p < 0.003 for any classical method compared to our method",
+      "D": "89.5±2.5 % for Aa, 86.2±2.2 % for Pg, 87.4±3.1 % for Sm.",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "The performance value finally reported"
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q1",
+    "question": "What are the main analytes type studied?",
+    "choices": {
+      "A": "AGP",
+      "B": "Protein",
+      "C": "Blood plasma",
+      "D": "Glycosylate",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Analytes",
+      "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q2",
+    "question": "What are the material and structure, or morphology of the SERS substrates used?",
+    "choices": {
+      "A": "Gold",
+      "B": "Sliver",
+      "C": "Surface plasmon polaritons",
+      "D": "AFM",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "SERS Substrates",
+      "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q3",
+    "question": "How many analytes are investigated?",
+    "choices": {
+      "A": "4",
+      "B": "3",
+      "C": "2",
+      "D": "1",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Number of Analytes",
+      "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q4",
+    "question": "What is the excitation laser wavelength used for SERS measurements?",
+    "choices": {
+      "A": "633 nm",
+      "B": "532 nm",
+      "C": "785 nm",
+      "D": "514 nm",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Laser wavelength",
+      "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q5",
+    "question": "What is the spectral range collected for the analysis of the analytes?",
+    "choices": {
+      "A": "300 cm⁻¹ to 2000 cm⁻¹",
+      "B": "300 cm⁻¹ to 3000 cm⁻¹",
+      "C": "400 cm⁻¹ to 3000 cm⁻¹",
+      "D": "400 cm⁻¹ to 4000 cm⁻¹",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spectral Range",
+      "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q6",
+    "question": "How many spectra are collected per analyte under each experimental condition?",
+    "choices": {
+      "A": "10",
+      "B": "80",
+      "C": "100",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Spectra for Each Analyte Under Each Condition",
+      "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q7",
+    "question": "What is the primary machine learning task addressed in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)"
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q8",
+    "question": "Which machine learning algorithm is implemented?",
+    "choices": {
+      "A": "ANN",
+      "B": "CNN",
+      "C": "DNN",
+      "D": "RNN",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q9",
+    "question": "What data splitting strategy is applied, and the parameters?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 7 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q10",
+    "question": "How many experimental replications are conducted to ensure reproducibility?",
+    "choices": {
+      "A": "371",
+      "B": "1733",
+      "C": "195",
+      "D": "487",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q11",
+    "question": "How many epochs are used during model training?",
+    "choices": {
+      "A": "50",
+      "B": "300",
+      "C": "1000",
+      "D": "3500",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q12",
+    "question": "What performance metrics are employed to evaluate the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "Precision",
+      "C": "Recall",
+      "D": "Predicted vs. Real Concentrations",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "2",
+    "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach",
+    "question_id": "Q13",
+    "question": "What are the reported performance values?",
+    "choices": {
+      "A": "Detection accuracy of 99.7 %",
+      "B": "97.8 % accuracy, >98.2 % precision, >98.2 % recall and >98.2 % F1 score",
+      "C": "p < 0.003",
+      "D": "A strong correlation",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "The performance value finally reported"
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q1",
+    "question": "What are the main analytes type studied?",
+    "choices": {
+      "A": "Chemical Detection",
+      "B": "2-dimensional physically activated chemical",
+      "C": "R800",
+      "D": "Single molecule",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Analytes",
+      "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q2",
+    "question": "What are the material and structure, or morphology of the SERS substrates used?",
+    "choices": {
+      "A": "Gold rod",
+      "B": "Sliver nanoparticles",
+      "C": "Gold nanoparticles",
+      "D": "Two-dimensional physically activated chemical",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "SERS Substrates",
+      "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q3",
+    "question": "How many analytes are investigated?",
+    "choices": {
+      "A": "4",
+      "B": "3",
+      "C": "2",
+      "D": "1",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Number of Analytes",
+      "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q4",
+    "question": "What is the excitation laser wavelength used for SERS measurements?",
+    "choices": {
+      "A": "633 nm",
+      "B": "532 nm",
+      "C": "785 nm",
+      "D": "514 nm",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Laser wavelength",
+      "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q5",
+    "question": "What is the spectral range collected for the analysis of the analytes?",
+    "choices": {
+      "A": "300 cm⁻¹ to 2000 cm⁻¹",
+      "B": "300 cm⁻¹ to 3000 cm⁻¹",
+      "C": "400 cm⁻¹ to 3000 cm⁻¹",
+      "D": "400 cm⁻¹ to 4000 cm⁻¹",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spectral Range",
+      "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q6",
+    "question": "How many spectra are collected per analyte under each experimental condition?",
+    "choices": {
+      "A": "2940",
+      "B": "64",
+      "C": "800",
+      "D": "50",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Spectra for Each Analyte Under Each Condition",
+      "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q7",
+    "question": "What is the primary machine learning task addressed in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)"
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q8",
+    "question": "Which machine learning algorithm is implemented?",
+    "choices": {
+      "A": "ANN",
+      "B": "CNN",
+      "C": "DNN",
+      "D": "RNN",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q9",
+    "question": "What data splitting strategy is applied, and the parameters?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q10",
+    "question": "How many experimental replications are conducted to ensure reproducibility?",
+    "choices": {
+      "A": "5",
+      "B": "256",
+      "C": "1000",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q11",
+    "question": "How many epochs are used during model training?",
+    "choices": {
+      "A": "50",
+      "B": "300",
+      "C": "1000",
+      "D": "3500",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q12",
+    "question": "What performance metrics are employed to evaluate the machine learning models?",
+    "choices": {
+      "A": "MSE",
+      "B": "R squre",
+      "C": "LOB",
+      "D": "Predicted vs. Real Concentrations",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "3",
+    "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks",
+    "question_id": "Q13",
+    "question": "What are the reported performance values?",
+    "choices": {
+      "A": "MSE, 0.111",
+      "B": "R squre, 0.958",
+      "C": "LOB, 1 fM",
+      "D": "LOQ, 10 fM",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "E",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "The performance value finally reported"
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q1",
+    "question": "What are the main analytes type studied?",
+    "choices": {
+      "A": "FASS",
+      "B": "buffer",
+      "C": "CoV NL63",
+      "D": "SARS-Cov-2",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Analytes",
+      "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q2",
+    "question": "What are the material and structure, or morphology of the SERS substrates used?",
+    "choices": {
+      "A": "Gold rod",
+      "B": "Sliver nanoparticles",
+      "C": "Gold nanoparticles",
+      "D": "Two-dimensional physically activated chemical",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "SERS Substrates",
+      "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q3",
+    "question": "How many analytes are investigated?",
+    "choices": {
+      "A": "4",
+      "B": "3",
+      "C": "2",
+      "D": "1",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Number of Analytes",
+      "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q4",
+    "question": "What is the excitation laser wavelength used for SERS measurements?",
+    "choices": {
+      "A": "633 nm",
+      "B": "532 nm",
+      "C": "785 nm",
+      "D": "514 nm",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Laser wavelength",
+      "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q5",
+    "question": "What is the spectral range collected for the analysis of the analytes?",
+    "choices": {
+      "A": "600 cm⁻¹ to 1800 cm⁻¹",
+      "B": "600 cm⁻¹ to 850 cm⁻¹",
+      "C": "912 cm⁻¹ to 1157 cm⁻¹",
+      "D": "600 cm⁻¹ to 1664 cm⁻¹",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spectral Range",
+      "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q6",
+    "question": "How many spectra are collected per analyte under each experimental condition?",
+    "choices": {
+      "A": "60",
+      "B": "20",
+      "C": "60 and 20",
+      "D": "2512 and 2360",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Spectra for Each Analyte Under Each Condition",
+      "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q7",
+    "question": "What is the primary machine learning task addressed in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Clustering",
+      "C": "Dimension reduction",
+      "D": "Classification",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)"
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q8",
+    "question": "Which machine learning algorithm is implemented?",
+    "choices": {
+      "A": "SVM",
+      "B": "CNN",
+      "C": "LR",
+      "D": "RNN",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q9",
+    "question": "What data splitting strategy is applied, and the parameters?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q10",
+    "question": "How many experimental replications are conducted to ensure reproducibility?",
+    "choices": {
+      "A": "5",
+      "B": "256",
+      "C": "1000",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q11",
+    "question": "How many epochs are used during model training?",
+    "choices": {
+      "A": "50",
+      "B": "170",
+      "C": "600",
+      "D": "1700",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q12",
+    "question": "What performance metrics are employed to evaluate the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "Precision",
+      "C": "Recall",
+      "D": "Predicted vs. Real Concentrations",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "4",
+    "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms",
+    "question_id": "Q13",
+    "question": "What are the reported performance values?",
+    "choices": {
+      "A": "0.993",
+      "B": "0.989",
+      "C": "0.977",
+      "D": "1.000",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "The performance value finally reported"
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q1",
+    "question": "What are the main analytes type studied?",
+    "choices": {
+      "A": "ACE2",
+      "B": "SARS-Cov-2 B1",
+      "C": "CoV NL63",
+      "D": "SARS-Cov-2",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Analytes",
+      "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q2",
+    "question": "What are the material and structure, or morphology of the SERS substrates used?",
+    "choices": {
+      "A": "Gold rod",
+      "B": "Sliver nanoparticles",
+      "C": "Gold nanoparticles",
+      "D": "Two-dimensional physically activated chemical",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "SERS Substrates",
+      "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q3",
+    "question": "How many analytes are investigated?",
+    "choices": {
+      "A": "4",
+      "B": "3",
+      "C": "2",
+      "D": "1",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Number of Analytes",
+      "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q4",
+    "question": "What is the excitation laser wavelength used for SERS measurements?",
+    "choices": {
+      "A": "633 nm",
+      "B": "532 nm",
+      "C": "785 nm",
+      "D": "514 nm",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Laser wavelength",
+      "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q5",
+    "question": "What is the spectral range collected for the analysis of the analytes?",
+    "choices": {
+      "A": "600 cm⁻¹ to 1800 cm⁻¹",
+      "B": "400 cm⁻¹ to 1850 cm⁻¹",
+      "C": "400 cm⁻¹ to 1800 cm⁻¹",
+      "D": "600 cm⁻¹ to 1850 cm⁻¹",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Spectral Range",
+      "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q6",
+    "question": "How many spectra are collected per analyte under each experimental condition?",
+    "choices": {
+      "A": "Less than 200",
+      "B": "200",
+      "C": "More than 200",
+      "D": "1200",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Spectra for Each Analyte Under Each Condition",
+      "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q7",
+    "question": "What is the primary machine learning task addressed in this study?",
+    "choices": {
+      "A": "Regression",
+      "B": "Classification",
+      "C": "Dimension reduction",
+      "D": "Classification and regression",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning task",
+      "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)"
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q8",
+    "question": "Which machine learning algorithm is implemented?",
+    "choices": {
+      "A": "ANN",
+      "B": "CNN",
+      "C": "DNN",
+      "D": "RNN",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Machine Learning algorithm",
+      "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q9",
+    "question": "What data splitting strategy is applied, and the parameters?",
+    "choices": {
+      "A": "T-T split; 0.7, 0.3",
+      "B": "T-D-T split; 0.8, 0.1, 0.1",
+      "C": "K-fold; 5 fold",
+      "D": "K-fold; 10 fold",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Splitting strategy (tdt split or k-fold)",
+      "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q10",
+    "question": "How many experimental replications are conducted to ensure reproducibility?",
+    "choices": {
+      "A": "10",
+      "B": "256",
+      "C": "1000",
+      "D": "35",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Number of Replications",
+      "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q11",
+    "question": "How many epochs are used during model training?",
+    "choices": {
+      "A": "100",
+      "B": "200",
+      "C": "300",
+      "D": "4700",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Number of Epochs",
+      "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q12",
+    "question": "What performance metrics are employed to evaluate the machine learning models?",
+    "choices": {
+      "A": "Accuracy",
+      "B": "R squre",
+      "C": "Recall",
+      "D": "MAE",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance metrics",
+      "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness."
+    }
+  },
+  {
+    "subject": "Physics - Surface Enhanced Raman Spectroscopy",
+    "paper_id": "5",
+    "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms",
+    "question_id": "Q13",
+    "question": "What are the reported performance values?",
+    "choices": {
+      "A": "Acc, 0.999; R square 0.98",
+      "B": "Acc, 0.999",
+      "C": "R square 0.98",
+      "D": "R square 0.993",
+      "E": "All of above",
+      "F": "None of above"
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "Performance values",
+      "term_explanation": "The performance value finally reported"
+    }
+  }
+]

data/public_health_infectious_disease_modeling.json

@@ -0,0 +1,1157 @@
+[
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q1",
+    "question": "What is the source location or country of origin for the data used in this study?",
+    "choices": {
+      "A": "Argentina",
+      "B": "Brazil",
+      "C": "South Africa",
+      "D": "China",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Source Data",
+      "term_explanation": "Numerical simulations of the model typically require real-world data as a foundation. In this case, we seek to understand the source of the data used."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q2",
+    "question": "What is the model used in this paper?",
+    "choices": {
+      "A": "second-order differential epidemic model",
+      "B": "SIS",
+      "C": "extended SEIR model",
+      "D": "SIR model",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Paper",
+      "term_explanation": "In general, studies of this nature are built upon extensions of compartmental models. We are interested in identifying the specific type of model employed in this work."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q3",
+    "question": "Into how many compartments is the population divided in the model?",
+    "choices": {
+      "A": "8",
+      "B": "6",
+      "C": "5",
+      "D": "7",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Compartments",
+      "term_explanation": "A key aspect of extensions of compartmental models lies in dividing the total population into distinct categories. We would like to know the total number of categories defined in the model."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q4",
+    "question": "What is the initial susceptible population of the model?",
+    "choices": {
+      "A": "60,140,000",
+      "B": "60,069,540",
+      "C": "62,000",
+      "D": "8,000",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Susceptible population",
+      "term_explanation": "Numerical simulations of compartmental models require carefully specified initial conditions. In particular, knowing the initial size of the susceptible population is critical, as it strongly influences the early dynamics of disease transmission and the overall trajectory of the outbreak."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q5",
+    "question": "What is the initial infected population of the model?",
+    "choices": {
+      "A": "8,000",
+      "B": "100",
+      "C": "360",
+      "D": "8,460",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Infected population",
+      "term_explanation": "The initial number of infected individuals plays a key role in shaping the model’s early behavior and is essential for understanding the potential outbreak size and dynamics."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q6",
+    "question": "What is the transmission rate?",
+    "choices": {
+      "A": "0.8999/day for symptomatically-infectious individuals",
+      "B": "0.7800/day for asymptomatically-infectious individuals",
+      "C": "0.8999/day for asymptomatically-infectious individuals",
+      "D": "0.4959/day for asymptomatically-infectious individuals",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Transmission rate",
+      "term_explanation": "Infectious disease models depend on key epidemiological parameters to capture the mechanisms of disease spread. The transmission rate, in particular, quantifies how rapidly the disease propagates through the population and is fundamental to the model’s predictive power."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q7",
+    "question": "What is the disease-induced mortality rate?",
+    "choices": {
+      "A": "0.8447/day",
+      "B": "1/(365*64.38)/day",
+      "C": "0.0101/day",
+      "D": "0.0015/day",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "mortality rate",
+      "term_explanation": "The disease-induced mortality rate specifies the proportion of infected individuals who succumb to the disease. Incorporating this parameter into simulations is vital for predicting the health burden of the disease and for evaluating intervention strategies."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q8",
+    "question": "What values of the basic reproduction number were considered in the model?",
+    "choices": {
+      "A": "2.1107",
+      "B": "<1",
+      "C": "0.02",
+      "D": "0",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "reproduction number",
+      "term_explanation": "One of the fundamental parameters incorporated into compartmental models is the reproduction number, which critically influences the future trajectory of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q9",
+    "question": "How many interventions are addressed in the paper?",
+    "choices": {
+      "A": ">3",
+      "B": "3",
+      "C": "2",
+      "D": "1",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "intervention",
+      "term_explanation": "The outcomes of model fitting are typically analyzed to assess the effects of intervention measures on the progression of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q10",
+    "question": "What are the novel contributions of the paper?",
+    "choices": {
+      "A": "A COVID-19 infection model with vaccination has been discussed",
+      "B": "Study the unreported COVID-19 cases",
+      "C": "Use the omicron feature to construct the model",
+      "D": "This is the first study to propose a second-order differential epidemic model.",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "innovations",
+      "term_explanation": "The novel contributions identify the paper’s unique value and justify its significance within the existing body of research."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "1",
+    "paper_title": "Mathematical modeling and analysis of COVID-19: A study of new variant Omicron",
+    "question_id": "Q11",
+    "question": "What are the limitations of the paper?",
+    "choices": {
+      "A": "Asymptomatic infections were not incorporated into the analysis",
+      "B": "The study did not assess the influence of the incubation period on the basic reproduction number",
+      "C": "The natural birth rate was not incorporated into the model",
+      "D": "This study did not examine how variations in the transmission rate influence the basic reproduction number",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "limitations",
+      "term_explanation": "The limitations of the paper reveal its potential weaknesses and help assess the validity, generalizability, and scope of the findings."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q1",
+    "question": "What is the source location or country of origin for the data used in this study?",
+    "choices": {
+      "A": "Bangladesh",
+      "B": "India",
+      "C": "China",
+      "D": "U.S.",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Source Data",
+      "term_explanation": "Numerical simulations of the model typically require real-world data as a foundation. In this case, we seek to understand the source of the data used."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q2",
+    "question": "What is the model used in this paper?",
+    "choices": {
+      "A": "SIR",
+      "B": "SIS",
+      "C": "extended SEIR model",
+      "D": "standard SEIR model",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Paper",
+      "term_explanation": "In general, studies of this nature are built upon extensions of compartmental models. We are interested in identifying the specific type of model employed in this work."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q3",
+    "question": "Into how many compartments is the population divided in the model?",
+    "choices": {
+      "A": "8",
+      "B": "4",
+      "C": "5",
+      "D": "7",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Compartments",
+      "term_explanation": "A key aspect of extensions of compartmental models lies in dividing the total population into distinct categories. We would like to know the total number of categories defined in the model."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q4",
+    "question": "What is the initial susceptible population of the model?",
+    "choices": {
+      "A": "1,352,642,280",
+      "B": "2,87,131",
+      "C": "111",
+      "D": "16",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Susceptible population",
+      "term_explanation": "Numerical simulations of compartmental models require carefully specified initial conditions. In particular, knowing the initial size of the susceptible population is critical, as it strongly influences the early dynamics of disease transmission and the overall trajectory of the outbreak."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q5",
+    "question": "What is the initial infected population of the model?",
+    "choices": {
+      "A": "16",
+      "B": "10",
+      "C": "3",
+      "D": "32",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Infected population",
+      "term_explanation": "The initial number of infected individuals plays a key role in shaping the model’s early behavior and is essential for understanding the potential outbreak size and dynamics."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q6",
+    "question": "What is the transmission rate?",
+    "choices": {
+      "A": "0.94/day",
+      "B": "1.11525/day",
+      "C": "0.80576/day",
+      "D": "0.24176/day",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Transmission rate",
+      "term_explanation": "Infectious disease models depend on key epidemiological parameters to capture the mechanisms of disease spread. The transmission rate, in particular, quantifies how rapidly the disease propagates through the population and is fundamental to the model’s predictive power."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q7",
+    "question": "What is the disease-induced mortality rate?",
+    "choices": {
+      "A": "0.51323/day",
+      "B": "0.04142/day",
+      "C": "0.88689/day",
+      "D": "0.26190/day",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "mortality rate",
+      "term_explanation": "The disease-induced mortality rate specifies the proportion of infected individuals who succumb to the disease. Incorporating this parameter into simulations is vital for predicting the health burden of the disease and for evaluating intervention strategies."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q8",
+    "question": "What values of the basic reproduction number were considered in the model?",
+    "choices": {
+      "A": "2.39745",
+      "B": "1.317554127",
+      "C": "0.2915951005",
+      "D": "0.1621143316",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "reproduction number",
+      "term_explanation": "One of the fundamental parameters incorporated into compartmental models is the reproduction number, which critically influences the future trajectory of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q9",
+    "question": "How many interventions are addressed in the paper?",
+    "choices": {
+      "A": "4",
+      "B": "3",
+      "C": "2",
+      "D": "1",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "intervention",
+      "term_explanation": "The outcomes of model fitting are typically analyzed to assess the effects of intervention measures on the progression of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q10",
+    "question": "What are the novel contributions of the paper?",
+    "choices": {
+      "A": "The study explores the role of lockdown interventions in mitigating the transmission of COVID-19 in China",
+      "B": "The study examines the impact of vaccination on the dynamics of the COVID-19 pandemic in India.",
+      "C": "This is the first study that examines the transmission mechanism of COVID-19 through a deterministic compartmental modeling framework",
+      "D": "The study investigates potential intervention strategies to control the COVID-19 epidemic in India and analyzes its projected future trajectories.",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "innovations",
+      "term_explanation": "The novel contributions identify the paper’s unique value and justify its significance within the existing body of research."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "2",
+    "paper_title": "COVID-19 pandemic in India: a mathematical model study",
+    "question_id": "Q11",
+    "question": "What are the limitations of the paper?",
+    "choices": {
+      "A": "The data employed in this study carries substantial uncertainty, which may affect the robustness of the results",
+      "B": "The study does not analyze the stability of the equilibrium states",
+      "C": "The model has not been validated using real-world data",
+      "D": "The study does not examine how variations in model parameters affect the basic reproduction number",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "limitations",
+      "term_explanation": "The limitations of the paper reveal its potential weaknesses and help assess the validity, generalizability, and scope of the findings."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q1",
+    "question": "What is the source location or country of origin for the data used in this study?",
+    "choices": {
+      "A": "Indonesia",
+      "B": "India",
+      "C": "Hubei",
+      "D": "Pakistan",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Source Data",
+      "term_explanation": "Numerical simulations of the model typically require real-world data as a foundation. In this case, we seek to understand the source of the data used."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q2",
+    "question": "What is the model used in this paper?",
+    "choices": {
+      "A": "SIR",
+      "B": "SIS",
+      "C": "extended SEIR model",
+      "D": "standard SEIR model",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Paper",
+      "term_explanation": "In general, studies of this nature are built upon extensions of compartmental models. We are interested in identifying the specific type of model employed in this work."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q3",
+    "question": "Into how many compartments is the population divided in the model?",
+    "choices": {
+      "A": "8",
+      "B": "6",
+      "C": "5",
+      "D": "7",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Compartments",
+      "term_explanation": "A key aspect of extensions of compartmental models lies in dividing the total population into distinct categories. We would like to know the total number of categories defined in the model."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q4",
+    "question": "What is the initial susceptible population of the model?",
+    "choices": {
+      "A": "100",
+      "B": "10 million",
+      "C": "10,000,505",
+      "D": "500",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Susceptible population",
+      "term_explanation": "Numerical simulations of compartmental models require carefully specified initial conditions. In particular, knowing the initial size of the susceptible population is critical, as it strongly influences the early dynamics of disease transmission and the overall trajectory of the outbreak."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q5",
+    "question": "What is the initial infected population of the model?",
+    "choices": {
+      "A": "205",
+      "B": "100",
+      "C": "5",
+      "D": "200",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Infected population",
+      "term_explanation": "The initial number of infected individuals plays a key role in shaping the model’s early behavior and is essential for understanding the potential outbreak size and dynamics."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q6",
+    "question": "What is the transmission rate?",
+    "choices": {
+      "A": "0.082/(people*day) for symptomatically-infectious individuals",
+      "B": "0.19/(people*day) for asymptomatically-infectious individuals",
+      "C": "1.727*10^(-7)/(people*day) for asymptomatically-infectious individuals",
+      "D": "1/(365*65)/(people*day) for all infectious individuals",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Transmission rate",
+      "term_explanation": "Infectious disease models depend on key epidemiological parameters to capture the mechanisms of disease spread. The transmission rate, in particular, quantifies how rapidly the disease propagates through the population and is fundamental to the model’s predictive power."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q7",
+    "question": "What is the disease-induced mortality rate?",
+    "choices": {
+      "A": "0.2/day",
+      "B": "1/(365*65)/day",
+      "C": "0.08195785/day",
+      "D": "0.1/day",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "mortality rate",
+      "term_explanation": "The disease-induced mortality rate specifies the proportion of infected individuals who succumb to the disease. Incorporating this parameter into simulations is vital for predicting the health burden of the disease and for evaluating intervention strategies."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q8",
+    "question": "What values of the basic reproduction number were considered in the model?",
+    "choices": {
+      "A": "0.1",
+      "B": "3.180126127",
+      "C": "0.01",
+      "D": "0.05",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "reproduction number",
+      "term_explanation": "One of the fundamental parameters incorporated into compartmental models is the reproduction number, which critically influences the future trajectory of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q9",
+    "question": "How many interventions are addressed in the paper?",
+    "choices": {
+      "A": "4",
+      "B": "3",
+      "C": "2",
+      "D": "1",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "intervention",
+      "term_explanation": "The outcomes of model fitting are typically analyzed to assess the effects of intervention measures on the progression of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q10",
+    "question": "What are the novel contributions of the paper?",
+    "choices": {
+      "A": "The model incorporates the effect of waning immunity in recovered individuals, allowing for the possibility of reinfection",
+      "B": "This is the first SEIR model to include both symptomatic and asymptomatic compartments",
+      "C": "An SEIR compartmental model is employed to investigate the dynamics of the COVID-19 outbreak in Pakistan",
+      "D": "The study found evidence that certain recovered individuals experienced reinfection with COVID-19",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "innovations",
+      "term_explanation": "The novel contributions identify the paper’s unique value and justify its significance within the existing body of research."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "3",
+    "paper_title": "A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity",
+    "question_id": "Q11",
+    "question": "What are the limitations of the paper?",
+    "choices": {
+      "A": "The study does not analyze how various intervention strategies  influence the outcomes of the model",
+      "B": "The model was not validated using data from multiple geographic regions, which may limit the generalizability of the findings",
+      "C": "The model does not account for several important parameters that may significantly affect the transmission dynamics of COVID-19",
+      "D": "The study does not investigate the potential existence of backward bifurcation within the model, which may have important implications for disease control thresholds",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "limitations",
+      "term_explanation": "The limitations of the paper reveal its potential weaknesses and help assess the validity, generalizability, and scope of the findings."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q1",
+    "question": "What is the source location or country of origin for the data used in this study?",
+    "choices": {
+      "A": "U.S.",
+      "B": "Nigeria",
+      "C": "UK",
+      "D": "Wuhan",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Source Data",
+      "term_explanation": "Numerical simulations of the model typically require real-world data as a foundation. In this case, we seek to understand the source of the data used."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q2",
+    "question": "What is the model used in this paper?",
+    "choices": {
+      "A": "SIR",
+      "B": "SIS",
+      "C": "standard SEIR model",
+      "D": "extended SEIR model",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Paper",
+      "term_explanation": "In general, studies of this nature are built upon extensions of compartmental models. We are interested in identifying the specific type of model employed in this work."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q3",
+    "question": "Into how many compartments is the population divided in the model?",
+    "choices": {
+      "A": "6",
+      "B": "4",
+      "C": "5",
+      "D": "7",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Compartments",
+      "term_explanation": "A key aspect of extensions of compartmental models lies in dividing the total population into distinct categories. We would like to know the total number of categories defined in the model."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q4",
+    "question": "What is the initial susceptible population of the model?",
+    "choices": {
+      "A": "2.5 million in Nigeria",
+      "B": "4.5 million in Nigeria",
+      "C": "3 million in Nigeria",
+      "D": "1,500,000 in Nigeria",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Susceptible population",
+      "term_explanation": "Numerical simulations of compartmental models require carefully specified initial conditions. In particular, knowing the initial size of the susceptible population is critical, as it strongly influences the early dynamics of disease transmission and the overall trajectory of the outbreak."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q5",
+    "question": "What is the initial infected population of the model?",
+    "choices": {
+      "A": "0",
+      "B": "1",
+      "C": "3",
+      "D": "4",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Infected population",
+      "term_explanation": "The initial number of infected individuals plays a key role in shaping the model’s early behavior and is essential for understanding the potential outbreak size and dynamics."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q6",
+    "question": "What is the transmission rate?",
+    "choices": {
+      "A": "1.082/day for symptomatically-infectious individuals in Nigeria",
+      "B": "1.082/day for asymptomatically-infectious individuls in Nigeria",
+      "C": "0.288/day for symptomatically-infectious individuals in Nigeria",
+      "D": "0.302/day for symptomatically-infectious individuals in Nigeria",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Transmission rate",
+      "term_explanation": "Infectious disease models depend on key epidemiological parameters to capture the mechanisms of disease spread. The transmission rate, in particular, quantifies how rapidly the disease propagates through the population and is fundamental to the model’s predictive power."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q7",
+    "question": "What is the disease-induced mortality rate?",
+    "choices": {
+      "A": "0.439/day for infectious individuals in Nigeria",
+      "B": "0.065/day for infectious individuals in Nigeria",
+      "C": "0.151/day for infectious individuals in Nigeria",
+      "D": "0.288/day for infectious individuals in Nigeria",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "mortality rate",
+      "term_explanation": "The disease-induced mortality rate specifies the proportion of infected individuals who succumb to the disease. Incorporating this parameter into simulations is vital for predicting the health burden of the disease and for evaluating intervention strategies."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q8",
+    "question": "What values of the basic reproduction number were considered in the model?",
+    "choices": {
+      "A": "7.481 in Nigeria",
+      "B": "1.878 in Nigeria",
+      "C": "1.98 in Nigeria",
+      "D": "1.675 in Nigeria",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "reproduction number",
+      "term_explanation": "One of the fundamental parameters incorporated into compartmental models is the reproduction number, which critically influences the future trajectory of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q9",
+    "question": "How many interventions are addressed in the paper?",
+    "choices": {
+      "A": "4",
+      "B": "3",
+      "C": "2",
+      "D": "1",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "intervention",
+      "term_explanation": "The outcomes of model fitting are typically analyzed to assess the effects of intervention measures on the progression of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q10",
+    "question": "What are the novel contributions of the paper?",
+    "choices": {
+      "A": "The study investigates the public health and socio-economic burden of the COVID-19 pandemic in Nigeria",
+      "B": "The study assesses the effects of pharmaceutical intervention strategies on the dynamics of the COVID-19 outbreak in Nigeria",
+      "C": "This study aims to determine the effectiveness of NPIs in controlling the spread of COVID-19 in Nigeria",
+      "D": "This study evaluates the impact of NPIs on the transmission dynamics of COVID-19 in New York",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "innovations",
+      "term_explanation": "The novel contributions identify the paper’s unique value and justify its significance within the existing body of research."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "4",
+    "paper_title": "Mathematical modeling and analysis of COVID-19 pandemic in Nigeria",
+    "question_id": "Q11",
+    "question": "What are the limitations of the paper?",
+    "choices": {
+      "A": "The effect of pharmaceutical interventions was not incorporated into the model",
+      "B": "The model may underestimate the true public health and socio-economic burden of the COVID-19 pandemic in Nigeria",
+      "C": "The model appears to considerably overestimate the actual public health burden of COVID-19 in Nigeria",
+      "D": "The parameters of the model were not rigorously or appropriately estimated",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "limitations",
+      "term_explanation": "The limitations of the paper reveal its potential weaknesses and help assess the validity, generalizability, and scope of the findings."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q1",
+    "question": "What is the source location or country of origin for the data used in this study?",
+    "choices": {
+      "A": "Portuguese",
+      "B": "Spain",
+      "C": "European",
+      "D": "Wuhan, China",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Data characteristics & collection",
+      "question_key_term": "Source Data",
+      "term_explanation": "Numerical simulations of the model typically require real-world data as a foundation. In this case, we seek to understand the source of the data used."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q2",
+    "question": "What is the model used in this paper?",
+    "choices": {
+      "A": "SIR",
+      "B": "SIS",
+      "C": "standard SEIR model",
+      "D": "extended SEIR model",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Paper",
+      "term_explanation": "In general, studies of this nature are built upon extensions of compartmental models. We are interested in identifying the specific type of model employed in this work."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q3",
+    "question": "Into how many compartments is the population divided in the model?",
+    "choices": {
+      "A": "6",
+      "B": "8",
+      "C": "9",
+      "D": "7",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Compartments",
+      "term_explanation": "A key aspect of extensions of compartmental models lies in dividing the total population into distinct categories. We would like to know the total number of categories defined in the model."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q4",
+    "question": "What is the initial susceptible population of the model?",
+    "choices": {
+      "A": "44000",
+      "B": "11000000",
+      "C": "43994",
+      "D": "10999994",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Susceptible population",
+      "term_explanation": "Numerical simulations of compartmental models require carefully specified initial conditions. In particular, knowing the initial size of the susceptible population is critical, as it strongly influences the early dynamics of disease transmission and the overall trajectory of the outbreak."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q5",
+    "question": "What is the initial infected population of the model?",
+    "choices": {
+      "A": "6",
+      "B": "1",
+      "C": "5",
+      "D": "0",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "Infected population",
+      "term_explanation": "The initial number of infected individuals plays a key role in shaping the model’s early behavior and is essential for understanding the potential outbreak size and dynamics."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q6",
+    "question": "What is the transmission rate?",
+    "choices": {
+      "A": "7.65/day from infected individuals",
+      "B": "2.55/day from infected individuals",
+      "C": "0.963/day from infected individuals",
+      "D": "10.2/day",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "Transmission rate",
+      "term_explanation": "Infectious disease models depend on key epidemiological parameters to capture the mechanisms of disease spread. The transmission rate, in particular, quantifies how rapidly the disease propagates through the population and is fundamental to the model’s predictive power."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q7",
+    "question": "What is the disease-induced mortality rate?",
+    "choices": {
+      "A": "3.5/day for all people",
+      "B": "1/day due to super-spreaders",
+      "C": "0.3/day due to super-spreaders",
+      "D": "4.8/day for all people",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "B",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "mortality rate",
+      "term_explanation": "The disease-induced mortality rate specifies the proportion of infected individuals who succumb to the disease. Incorporating this parameter into simulations is vital for predicting the health burden of the disease and for evaluating intervention strategies."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q8",
+    "question": "What values of the basic reproduction number were considered in the model?",
+    "choices": {
+      "A": "0.945",
+      "B": ">1",
+      "C": "0.631",
+      "D": "0.25",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "A",
+    "metadata": {
+      "Task-oriented Category": "Technical approach & details",
+      "question_key_term": "reproduction number",
+      "term_explanation": "One of the fundamental parameters incorporated into compartmental models is the reproduction number, which critically influences the future trajectory of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q9",
+    "question": "How many interventions are addressed in the paper?",
+    "choices": {
+      "A": "4",
+      "B": "3",
+      "C": "2",
+      "D": "1",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "F",
+    "metadata": {
+      "Task-oriented Category": "Study subject & experimental setup",
+      "question_key_term": "intervention",
+      "term_explanation": "The outcomes of model fitting are typically analyzed to assess the effects of intervention measures on the progression of the epidemic."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q10",
+    "question": "What are the novel contributions of the paper?",
+    "choices": {
+      "A": "This is the first study to propose an epidemiological compartment model",
+      "B": "The study compares the effectiveness of various intervention measures in controlling the epidemic",
+      "C": "The model incorporates a category of super-spreaders",
+      "D": "The model incorporates the viral load of the infectious",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "C",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "innovations",
+      "term_explanation": "The novel contributions identify the paper’s unique value and justify its significance within the existing body of research."
+    }
+  },
+  {
+    "subject": "Public Health - Infectious-disease Modeling",
+    "paper_id": "5",
+    "paper_title": "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan",
+    "question_id": "Q11",
+    "question": "What are the limitations of the paper?",
+    "choices": {
+      "A": "The model does not account for hospitalized individuals",
+      "B": "The model does not analyze the existence or properties of the disease-free equilibrium",
+      "C": "The study does not examine how variations in model parameters affect the basic reproduction number",
+      "D": "The study was constrained by limited data availability during its early stages",
+      "E": "All of above.",
+      "F": "None of above."
+    },
+    "answer": "D",
+    "metadata": {
+      "Task-oriented Category": "Conclusions & results",
+      "question_key_term": "limitations",
+      "term_explanation": "The limitations of the paper reveal its potential weaknesses and help assess the validity, generalizability, and scope of the findings."
+    }
+  }
+]