add statistics

.gitignore

@@ -3,7 +3,6 @@
 upload_huggingface.py
 # only for github repo, not huggingface repo
 # sci_volume_data/**/*.raw
-statistics/
 .cache/
 .claude/
 .vscode/

statistics/SciVisAgentBench_Taxnomoy.txt

@@ -0,0 +1,211 @@
+Data Taxonomy (several cross-cutting categories):
+* Application Domains (this shouldn’t necessarily be a category rather a way to indicate the source of the data)
+   * Climate data
+   * SEM image data
+   * CT scan for objects
+   * Medical/CT/MRI scan
+   * Simulation data
+   * Molecular data
+   * ….
+
+
+* Data Type
+   * Structured Data
+      * Image data (uniform rectilinear grids)
+      * Rectilinear grids
+      * Structured grids (curvilinear)
+      * AMR (Adaptive Mesh Refinement)?
+   * Unstructured Data
+      * Unstructured grids (arbitrary cell types)
+      * Polygonal data (surfaces, meshes)
+      * Point clouds
+   * Specialized Types
+      * Hyper-tree grids
+      * Composite/multi-block datasets
+      * Graph/network data
+
+
+* Temporal Dimension
+   * Static/Single-Timestep
+      * Analysis of a single snapshot
+      * Spatial patterns only
+   * Time-Series
+      * Multiple discrete steps
+      * Temporal sequences
+
+
+* Attribute Types (how the data is represented, what information it has) 
+   * Scalar Fields (single value per point/cell)
+      * Temperature, pressure, density
+      * Scalars driving isosurfaces, color mapping
+   * Vector Fields (3-component direction/magnitude)
+      * Velocity, force, displacement
+      * Visualized via streamlines, glyphs, and LIC
+   * Tensor Fields (matrix at each point)
+      * Stress, strain, diffusion tensors
+      * Visualized via tensor glyphs, eigenvalue analysis
+   * Multi-variate/Multi-field/Multi-modal
+      * Multiple scalar/vector fields
+      * High-dimensional data visualization by analyzing multiple dimensions all at once
+* Data Ensemble
+   * Multiple runs, parameter sweeps, etc.
+
+
+
+
+
+
+Tasks Taxonomy: 
+Top level: 1) Atomic Operation, 2) Workflow, 3) Scientific Insights
+
+
+1) Atomic Operation (individual well-defined operations):
+* Extraction & Subsetting (Isolate regions of interest from larger datasets)
+   * Spatial/temporal Extraction
+      * Clipping (by plane, box, sphere, implicit function)
+      * Region selection (by point/cell IDs, bounding box)
+      * VOI extraction (volume of interest from structured data)
+   * Value-Based Selection
+      * Thresholding (scalar range filtering)
+      * Isocontouring/isosurfacing (constant value extraction)
+      * Connectivity filtering (connected region extraction)
+   * Sampling
+      * Subsampling/decimation (reduce resolution)
+      * Probing (sampling at specific points)
+      * Masking (regular/irregular point selection)
+* Geometry & Topology Transformation (Change the structure or shape of data without necessarily changing attribute values)
+   * Geometric Modification
+      * Translation, rotation, scaling
+      * Deformation, warping
+      * Point/vertex manipulation
+   * Topological Changes
+      * Triangulation (convert polygons to triangles), Tessellation/subdivision
+      * Mesh refinement/coarsening
+      * Cell type conversion
+      * Boundary extraction (surface from volume)
+   * Structural Operations
+      * Merging datasets
+      * Appending data
+      * Splitting/partitioning
+* Attribute Computation & Derivation (Calculate new data attributes from existing ones)
+   * Field Derivatives
+      * Gradient computation
+      * Divergence, curl, vorticity
+      * Curvature calculation
+      * Normal generation
+   * Scalar Operations
+      * Arithmetic operations on fields
+      * Vector magnitude computation
+      * Component extraction
+      * Field aggregation/statistics
+   * Advanced Computations[a]
+      * Tensor operations (eigenvalues, eigenvectors)
+      * Interpolation between fields
+      * Distance computations
+   * Time-Dependent Processing
+      * Temporal interpolation
+      * Particle tracing through time
+      * Flow integration [b](streamlines, pathlines, streaklines, timelines)
+      * Temporal statistics/aggregation
+      * Flow map computation
+* Representation & Mapping (Transform data into visual representations)
+   * Glyph-Based Representation
+      * Oriented glyphs (arrows, cones)
+      * Scaled glyphs (size-mapped symbols)
+      * Tensor glyphs (ellipsoids, superquadrics)
+      * Volume markers
+   * Geometric Primitives
+      * Isosurfaces
+      * Contour lines/surfaces
+      * Cut planes/slices
+      * Ribbons, tubes, streamlines
+   * Color & Texture Mapping
+      * Scalar to color mapping
+      * Texture coordinate generation
+      * Opacity mapping
+   * Volume Representations
+      * Ray casting
+      * Splatting
+* Smoothing & enhancement (Improve data quality or visual appearance)
+   * Smoothing Operations
+      * Surface smoothing (Laplacian, Gaussian)
+      * Data noise reduction
+      * Interpolation for filling gaps
+   * Enhancement
+      * Edge enhancement/detection
+      * Feature extraction
+      * Sharpening
+   * Filtering 
+      * Outlier removal
+      * Statistical filtering
+* View / Rendering Manipulation
+   * View Changes
+      * Rotation, zoom, move
+   * Render Options (should be belong to transformation)
+      * Light position
+      * Lighting mode
+
+
+2) Workflow (sequence of operations with a clear visualization/analysis goal)
+* Data Understanding & Exploration
+   * Data Characterization (statistics, distributions, quality check)
+   * Spatial Exploration (slicing, probing, overview generation)
+   * Feature Discovery (identifying interesting structures/patterns)
+* Analysis & Quantification
+   * Statistical Analysis (descriptive statistics, distributions, correlations)
+   * Region-Based Measurement (volumes, areas, integrated quantities)
+   * Profile & Cross-Section Analysis (1D/2D extraction from 3D)
+   * Derived Quantity Computation (gradients, vorticity, custom fields)
+* Feature Extraction & Tracking
+   * Structure Identification (vortices, shocks, boundaries, topology)
+   * Feature Characterization (properties, classification, quantification)
+   * Temporal Tracking (feature evolution, lifecycle, trajectories)
+* Comparative & Temporal Analysis
+   * Multi-Variable Comparison (correlation, coordinated views)
+   * Temporal Evolution / Comparison (time-series comparison)
+   * Simulation Comparison (parameter studies, model validation)
+   * Difference Analysis (error fields, change detection)
+* Flow & Transport Analysis[c]
+   * Trajectory Computation (streamlines, pathlines, streaklines)
+   * Lagrangian Analysis (particle tracking, residence time, FTLE)
+   * Transport Quantification (flux, mixing, coherent structures)
+* Verification & Validation
+   * Data Quality Assessment (outliers, artifacts, boundary conditions)
+   * Code Verification (convergence, analytical comparison, consistency)
+   * Physical Validation (experimental comparison, uncertainty quantification)
+* Data Processing & Optimization
+   * Data Conditioning (cleaning, smoothing, noise reduction)
+   * Data Reduction (decimation, sampling, compression)
+   * Format Conversion & Restructuring (mesh generation, type conversion)
+   * Parallel & Distributed Processing (HPC workflows, decomposition)[d]
+* Uncertainty Quantification & Visualization[e]
+   * Sensitivity analysis, uncertainty characterization (estimate uncertainty)
+   * Aggregation and summarization (visualize quantile and interval)
+* Communication & Dissemination
+   * Static Visualization (publication figures, high-resolution images)
+   * Animation Generation (temporal, spatial, parameter animations)
+   * Interactive Applications (web/desktop viewers, dashboards)
+   * Report Generation (automated analysis reports, summaries)
+
+
+3) Scientific Insights (analysis or visualization that leads to domain-relevant insights)
+* Application-specific questions and insights that can be derived from analysis or visualization
+* The result could be: binary decisions, multiple choices, 
+* Potentially involves different workflow steps
+
+
+
+
+
+
+
+
+What not to include 
+* Excessively large dataset (multiple GB to TB level)
+* Interaction that ties to a specific tool/interface
+* Questions do not have unique and clear answers/ground truth
+[a]probably need better title
+[b]Move flow integration into its own category of Vector Operations, analogous to Scalar Operations? Streamlines are not time-dependent. Also could include LIC, and maybe FTLE.
+[c]There is overlap between this category and previous atomic operations on flow integration (see my earlier comment).
+[d]Consider large scale HPC to be out of scope, in order to make the benchmark more broadly accessible?
+[e]Group together with V&V?

statistics/generate_full_report.py

@@ -0,0 +1,290 @@
+#!/usr/bin/env python3
+"""
+Generate comprehensive statistics report for SciVisAgentBench.
+Processes all CSV files and creates updated statistics.
+"""
+
+import pandas as pd
+import os
+from collections import defaultdict
+
+def load_csv_files(sheets_dir):
+    """Load all CSV files from the sheets directory."""
+    csv_files = {}
+
+    # Define which files to include (excluding old chatvis_bench and main)
+    files_to_include = [
+        'SciVisAgentBench_Statistics - bioimage_data.csv',
+        'SciVisAgentBench_Statistics - molecular_vis.csv',
+        'SciVisAgentBench_Statistics - sci_volume_data.csv',
+        'SciVisAgentBench_Statistics - topology.csv',
+        'SciVisAgentBench_Statistics - paraview.csv'  # New file
+    ]
+
+    for filename in files_to_include:
+        filepath = os.path.join(sheets_dir, filename)
+        if os.path.exists(filepath):
+            df = pd.read_csv(filepath)
+            # Extract key from filename
+            key = filename.replace('SciVisAgentBench_Statistics - ', '').replace('.csv', '')
+            csv_files[key] = df
+            print(f"Loaded {filename}: {len(df)} rows")
+
+    return csv_files
+
+
+def count_individual_tags(series, separator=';'):
+    """Count individual tags in a series of semicolon-separated values."""
+    counts = defaultdict(int)
+    for value in series.dropna():
+        if pd.isna(value) or str(value).strip() == '':
+            continue
+        # Support both ; and , as separators
+        value_str = str(value).replace(',', ';')
+        tags = [tag.strip() for tag in value_str.split(separator)]
+        for tag in tags:
+            if tag:
+                counts[tag] += 1
+    return dict(sorted(counts.items(), key=lambda x: -x[1]))
+
+def count_combinations(series):
+    """Count exact combinations as they appear."""
+    counts = defaultdict(int)
+    for value in series.dropna():
+        if pd.isna(value) or str(value).strip() == '':
+            continue
+        counts[str(value).strip()] += 1
+    return dict(sorted(counts.items(), key=lambda x: -x[1]))
+
+def generate_report(csv_files):
+    """Generate comprehensive statistics report."""
+
+    # Combine all dataframes
+    all_data = pd.concat(csv_files.values(), ignore_index=True)
+
+    # Filter only Task and Workflow levels (exclude Operation level)
+    cases_only = all_data[all_data['Task Level 1: Complexity Level'].isin(['Task', 'Workflow'])]
+
+    print(f"\nTotal rows in all files: {len(all_data)}")
+    print(f"Total cases (Task + Workflow): {len(cases_only)}")
+
+    # Generate statistics
+    stats = {}
+
+    # 1. Total Cases Count
+    complexity_counts = all_data['Task Level 1: Complexity Level'].value_counts()
+    stats['total_cases'] = len(cases_only)
+    stats['total_tasks'] = complexity_counts.get('Task', 0)
+    stats['total_workflows'] = complexity_counts.get('Workflow', 0)
+    stats['total_operations'] = complexity_counts.get('Operation', 0)
+
+    # File breakdown
+    stats['file_breakdown'] = {}
+    for name, df in csv_files.items():
+        # Calculate total operation count
+        cases_df = df[df['Task Level 1: Complexity Level'].isin(['Task', 'Workflow'])]
+
+        # Convert Operation Count to numeric, treating 'N/A' as NaN
+        operation_counts = pd.to_numeric(cases_df['Operation Count'], errors='coerce')
+        total_operations = operation_counts.sum() if len(operation_counts) > 0 else 0
+
+        file_stats = {
+            'total_rows': len(df),
+            'operations': len(df[df['Task Level 1: Complexity Level'] == 'Operation']),
+            'tasks': len(df[df['Task Level 1: Complexity Level'] == 'Task']),
+            'workflows': len(df[df['Task Level 1: Complexity Level'] == 'Workflow']),
+            'cases': len(cases_df),
+            'total_operations': int(total_operations)
+        }
+        stats['file_breakdown'][name] = file_stats
+
+    # 2. Application Domain Statistics (cases only)
+    stats['applications_individual'] = count_individual_tags(cases_only['Application'])
+    stats['applications_combinations'] = count_combinations(cases_only['Application'])
+
+    # 3. Data Type Statistics (cases only)
+    stats['data_types_individual'] = count_individual_tags(cases_only['Data'])
+    stats['data_types_combinations'] = count_combinations(cases_only['Data'])
+
+    # 4. Visualization Operations Statistics (cases only)
+    stats['operations_all'] = count_individual_tags(cases_only['Task Level 2: Visualization Operations'], separator=';')
+
+    # Operations by complexity level
+    tasks_only = cases_only[cases_only['Task Level 1: Complexity Level'] == 'Task']
+    workflows_only = cases_only[cases_only['Task Level 1: Complexity Level'] == 'Workflow']
+
+    stats['operations_task_level'] = count_individual_tags(tasks_only['Task Level 2: Visualization Operations'], separator=';')
+    stats['operations_workflow_level'] = count_individual_tags(workflows_only['Task Level 2: Visualization Operations'], separator=';')
+
+    # Also count operation combinations
+    stats['operations_combinations'] = count_combinations(cases_only['Task Level 2: Visualization Operations'])
+
+    return stats, cases_only, all_data
+
+def write_markdown_report(stats, output_path):
+    """Write statistics report to markdown file."""
+
+    with open(output_path, 'w') as f:
+        f.write("# SciVisAgentBench - Comprehensive Statistics Report\n")
+        f.write(f"*Generated from {len(stats['file_breakdown'])} CSV files in the benchmark*\n")
+        f.write("---\n")
+
+        # 1. Total Cases Count
+        f.write("## 1. Total Cases Count\n")
+        f.write("**Important**: Cases = Tasks + Workflows only (Operation-level entries are NOT counted as cases)\n\n")
+
+        f.write("### Overall Summary\n")
+        f.write(f"- **Total Tasks**: {stats['total_tasks']}\n")
+        f.write(f"- **Total Workflows**: {stats['total_workflows']}\n")
+        f.write(f"- **Total Cases**: **{stats['total_cases']}** (Tasks + Workflows)\n")
+        if stats['total_operations'] > 0:
+            f.write(f"- **Total Operations**: **{stats['total_operations']}** (Operation-level entries)\n")
+        f.write("\n")
+
+        f.write("### Breakdown by File\n")
+        f.write("| File | Tasks | Workflows | Cases (Task+Workflow) | Total Operations |\n")
+        f.write("|------|-------|-----------|----------------------|-----------------|\n")
+        total_tasks = 0
+        total_workflows = 0
+        total_cases = 0
+        total_operations_sum = 0
+        for name, file_stats in stats['file_breakdown'].items():
+            f.write(f"| {name} | {file_stats['tasks']} | {file_stats['workflows']} | "
+                   f"**{file_stats['cases']}** | {file_stats['total_operations']} |\n")
+            total_tasks += file_stats['tasks']
+            total_workflows += file_stats['workflows']
+            total_cases += file_stats['cases']
+            total_operations_sum += file_stats['total_operations']
+
+        f.write(f"| **TOTAL** | **{total_tasks}** | **{total_workflows}** | **{total_cases}** | **{total_operations_sum}** |\n")
+        f.write("\n")
+
+        # 2. Application Domain Statistics
+        f.write("## 2. Application Domain Statistics\n")
+        f.write("**Note**: These statistics include ONLY cases (Tasks + Workflows). Operation-level entries are excluded.\n\n")
+
+        f.write("### Individual Application Counts\n")
+        f.write("*(Counts individual tags, so multi-tagged entries contribute to multiple categories)*\n\n")
+        f.write("| Application | Count |\n")
+        f.write("|-------------|-------|\n")
+        for app, count in stats['applications_individual'].items():
+            f.write(f"| {app} | {count} |\n")
+        total_app_tags = sum(stats['applications_individual'].values())
+        f.write(f"\n**Total individual application tags**: {total_app_tags}\n\n")
+
+        f.write("### Application Combinations\n")
+        f.write("*(Shows exact combinations as they appear in the data)*\n\n")
+        f.write("| Application Combination | Count |\n")
+        f.write("|------------------------|-------|\n")
+        for combo, count in stats['applications_combinations'].items():
+            f.write(f"| {combo} | {count} |\n")
+        f.write("\n")
+
+        # 3. Data Type Statistics
+        f.write("## 3. Data Type Statistics\n")
+        f.write("**Note**: These statistics include ONLY cases (Tasks + Workflows). Operation-level entries are excluded.\n\n")
+
+        f.write("### Individual Data Type Counts\n")
+        f.write("*(Counts individual tags, so multi-tagged entries contribute to multiple categories)*\n\n")
+        f.write("| Data Type | Count |\n")
+        f.write("|-----------|-------|\n")
+        for dtype, count in stats['data_types_individual'].items():
+            f.write(f"| {dtype} | {count} |\n")
+        total_dtype_tags = sum(stats['data_types_individual'].values())
+        f.write(f"\n**Total individual data type tags**: {total_dtype_tags}\n\n")
+
+        f.write("### Data Type Combinations\n")
+        f.write("*(Shows exact combinations as they appear in the data)*\n\n")
+        f.write("| Data Type Combination | Count |\n")
+        f.write("|-----------------------|-------|\n")
+        for combo, count in stats['data_types_combinations'].items():
+            f.write(f"| {combo} | {count} |\n")
+        f.write("\n")
+
+        # 4. Complexity Level Statistics
+        f.write("## 4. Task Level 1: Complexity Level Statistics\n\n")
+        f.write("### Overall Distribution\n")
+        f.write("| Complexity Level | Entry Count | Counted as Case? |\n")
+        f.write("|------------------|-------------|------------------|\n")
+        if stats['total_operations'] > 0:
+            f.write(f"| Operation | {stats['total_operations']} | ❌ NO |\n")
+        f.write(f"| Task | {stats['total_tasks']} | ✅ YES |\n")
+        f.write(f"| Workflow | {stats['total_workflows']} | ✅ YES |\n")
+        f.write(f"| **Total Cases** | **{stats['total_cases']}** | **(Tasks + Workflows)** |\n")
+        f.write("\n")
+
+        # 5. Visualization Operations Statistics
+        f.write("## 5. Task Level 2: Visualization Operations Statistics\n")
+        f.write("**Note**: These statistics include ONLY cases (Tasks + Workflows). Operation-level entries are excluded.\n\n")
+
+        f.write("### All Visualization Operations (Sorted by Frequency)\n")
+        f.write("| Rank | Visualization Operation | Total Count |\n")
+        f.write("|------|------------------------|-------------|\n")
+        for i, (op, count) in enumerate(stats['operations_all'].items(), 1):
+            f.write(f"| {i} | {op} | {count} |\n")
+        total_op_tags = sum(stats['operations_all'].values())
+        f.write(f"\n**Total visualization operation tags**: {total_op_tags}\n\n")
+
+        f.write("### Top 10 Most Common Visualization Operations\n")
+        f.write("| Rank | Operation | Count |\n")
+        f.write("|------|-----------|-------|\n")
+        for i, (op, count) in enumerate(list(stats['operations_all'].items())[:10], 1):
+            f.write(f"| {i} | {op} | {count} |\n")
+        f.write("\n")
+
+        f.write("### Visualization Operations by Complexity Level (Cases Only)\n\n")
+
+        f.write("#### Task Level (Top 10)\n")
+        f.write("| Rank | Operation | Count |\n")
+        f.write("|------|-----------|-------|\n")
+        for i, (op, count) in enumerate(list(stats['operations_task_level'].items())[:10], 1):
+            f.write(f"| {i} | {op} | {count} |\n")
+        f.write("\n")
+
+        f.write("#### Workflow Level (Top 10)\n")
+        f.write("| Rank | Operation | Count |\n")
+        f.write("|------|-----------|-------|\n")
+        for i, (op, count) in enumerate(list(stats['operations_workflow_level'].items())[:10], 1):
+            f.write(f"| {i} | {op} | {count} |\n")
+        f.write("\n")
+
+        # 6. Summary Statistics
+        f.write("## 6. Summary Statistics\n")
+        f.write(f"- **Total number of CSV files analyzed**: {len(stats['file_breakdown'])}\n")
+        f.write(f"- **Total Cases (Tasks + Workflows)**: **{stats['total_cases']}**\n")
+        f.write(f"- **Unique application domains**: {len(stats['applications_individual'])}\n")
+        f.write(f"- **Unique data types**: {len(stats['data_types_individual'])}\n")
+        f.write(f"- **Unique visualization operations**: {len(stats['operations_all'])}\n\n")
+
+        f.write("### File Contributions\n")
+        f.write("| File | Cases Contributed | Percentage |\n")
+        f.write("|------|-------------------|------------|\n")
+        for name, file_stats in sorted(stats['file_breakdown'].items(),
+                                      key=lambda x: -x[1]['cases']):
+            percentage = (file_stats['cases'] / stats['total_cases'] * 100) if stats['total_cases'] > 0 else 0
+            f.write(f"| {name} | {file_stats['cases']} | {percentage:.1f}% |\n")
+        f.write("\n")
+
+def main():
+    # Paths
+    sheets_dir = '/Users/kuangshiai/Documents/ND-VIS/Code/SciVisAgentBench-tasks/statistics/sheets'
+    output_path = '/Users/kuangshiai/Documents/ND-VIS/Code/SciVisAgentBench-tasks/statistics/statistics_report.md'
+
+    # Load CSV files
+    print("Loading CSV files...")
+    csv_files = load_csv_files(sheets_dir)
+
+    # Generate statistics
+    print("\nGenerating statistics...")
+    stats, cases_only, all_data = generate_report(csv_files)
+
+    # Write markdown report
+    print(f"\nWriting report to {output_path}...")
+    write_markdown_report(stats, output_path)
+
+    print("\n✅ Report generation complete!")
+    print(f"   Total cases: {stats['total_cases']}")
+    print(f"   Total files: {len(csv_files)}")
+
+if __name__ == '__main__':
+    main()

statistics/sheets/SciVisAgentBench_Statistics - bioimage_data.csv

@@ -0,0 +1,16 @@
+Case Name,Application,Data,Task Level 1: Complexity Level,Task Level 2: Visualization Operations,Operation Count
+Napari - Load & Layer Management,Biology,Scalar Field; Multivariate; Time-varying,Task,Dataset Restructuring; Field Computation,2
+Napari - 3D/4D Navigation,Biology,Scalar Field; Multivariate; Time-varying,Task,Data Subsetting & Extraction; Temporal Processing,2
+Napari - Color & Opacity,Biology,Scalar Field; Multivariate; Time-varying,Task,Color & Opacity Mapping,1
+Napari - Contrast Adjustment,Biology,Scalar Field; Multivariate; Time-varying,Task,Color & Opacity Mapping; Data Smoothing & Filtering,2
+Napari - Camera Control,Biology,Scalar Field; Multivariate; Time-varying,Task,View & Camera Control; Field Computation,2
+Napari - Visibility & Screenshot,Biology,Scalar Field; Multivariate; Time-varying,Task,Data Subsetting & Extraction; View & Camera Control,2
+Napari - Annotations,Biology,Scalar Field; Multivariate; Time-varying,Task,Glyph & Marker Placement,1
+Napari - Scale Bar & Export,Biology,Scalar Field; Multivariate; Time-varying,Task,Glyph & Marker Placement; View & Camera Control,2
+Napari - Export & Cleanup,Biology,Scalar Field; Multivariate; Time-varying,Task,Dataset Restructuring,1
+Napari - Error Handling,Biology,Scalar Field; Multivariate; Time-varying,Task,Dataset Restructuring,1
+Napari - 3D Rendering,Biology,Scalar Field; Multivariate; Time-varying,Task,Surface & Contour Extraction; Volume Rendering; Dataset Restructuring,3
+Napari - Analysis Workflows,Biology,Scalar Field; Multivariate,Workflow,Field Computation; Glyph & Marker Placement; Feature Identification & Segmentation; Dataset Restructuring; Temporal Processing; Data Subsetting & Extraction,6
+Napari - Figure Recreation,Biology,Scalar Field; Multivariate,Workflow,Color & Opacity Mapping; Data Smoothing & Filtering; View & Camera Control,3
+Napari - Iso Surface Determination,Biology,Scalar Field; Multivariate,Workflow,Surface & Contour Extraction; Volume Rendering; View & Camera Control; Color & Opacity Mapping,4
+Napari - Visualization Workflows,Biology,Scalar Field; Multivariate,Workflow,Color & Opacity Mapping; Dataset Restructuring; Data Subsetting & Extraction; View & Camera Control; Surface & Contour Extraction; Volume Rendering; Data Smoothing & Filtering,7

statistics/sheets/SciVisAgentBench_Statistics - molecular_vis.csv

@@ -0,0 +1,16 @@
+Case Name,Application,Data,Task Level 1: Complexity Level,Task Level 2: Visualization Operations,Operation Count
+MD - Load & Basic Representations,Chemistry,Multi-variate,Task,Dataset Restructuring; Glyph & Marker Placement; Surface & Contour Extraction,3
+MD - Color by Properties,Chemistry,Multi-variate,Task,Color & Opacity Mapping,1
+MD - Region Selection,Chemistry,Multi-variate,Task,Data Subsetting & Extraction; Color & Opacity Mapping,2
+MD - Multi-Representation,Chemistry,Multi-variate,Task,Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping,3
+MD - Advanced Coloring,Chemistry,Multi-variate,Task,Color & Opacity Mapping; Data Subsetting & Extraction,2
+MD - Active Site Selection,Chemistry,Multi-variate,Task,Data Subsetting & Extraction; Field Computation; Color & Opacity Mapping; View & Camera Control,4
+MD - Spatial Queries,Chemistry,Multi-variate,Task,Data Subsetting & Extraction; Field Computation; Color & Opacity Mapping,3
+MD - Visibility Control,Chemistry,Multi-variate,Task,Data Subsetting & Extraction,1
+MD - Residue Selection,Chemistry,Multi-variate,Task,Data Subsetting & Extraction; Color & Opacity Mapping,2
+MD - Combined Representations,Chemistry,Multi-variate,Task,Data Subsetting & Extraction; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping,4
+MD - Distance-based Selection,Chemistry,Multi-variate,Task,Data Subsetting & Extraction; Field Computation; Color & Opacity Mapping,3
+MD - Gradient Coloring,Chemistry,Multi-variate,Task,Color & Opacity Mapping; Data Subsetting & Extraction,2
+MD - Multi-Criteria Selection,Chemistry,Multi-variate,Task,Data Subsetting & Extraction; Surface & Contour Extraction; Field Computation; Color & Opacity Mapping; Data Sampling & Resolution Control,5
+MD - curved-membrane,Chemistry; Biology,Multi-variate,Workflow,Scientific Insight Derivation; Data Subsetting & Extraction; Color & Opacity Mapping; View & Camera Control; Glyph & Marker Placement,5
+MD - ras-raf-membrane,Chemistry; Biology,Multi-variate,Workflow,Scientific Insight Derivation; Data Subsetting & Extraction; Color & Opacity Mapping; View & Camera Control; Glyph & Marker Placement,5

statistics/sheets/SciVisAgentBench_Statistics - paraview.csv

@@ -0,0 +1,49 @@
+Case Name,Application,Data,Task Level 1: Complexity Level,Task Level 2: Visualization Operations,Operation Count
+ABC,Physics,Scalar Fields; Vector Fields,Workflow,Temporal Processing; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control; Field Computation,6
+argon-bubble,Chemistry,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+Bernard,Physics,Vector Fields,Workflow,Temporal Processing; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control,5
+bonsai,Biology,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+carp,Biology,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+chameleon_isosurface,Biology,Scalar Fields,Workflow,Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control,3
+crayfish_streamline,Biology,Vector Fields,Workflow,Temporal Processing; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control,5
+engine,Physics,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+foot,Medical Science,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+lobster,Biology,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+mhd-magfield_streamribbon,Physics,Vector Fields,Workflow,Temporal Processing; Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control,4
+mhd-turbulence_pathline,Physics,Vector Fields; Time-varying,Workflow,Temporal Processing; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control,5
+mhd-turbulence_pathribbon,Physics,Vector Fields; Time-varying,Workflow,Temporal Processing; Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control,4
+mhd-turbulence_streamline,Physics,Vector Fields,Workflow,Temporal Processing; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control; Field Computation,6
+miranda,Physics,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+richtmyer,Physics,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+rotstrat,Physics,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+rti-velocity_glyph,Physics,Vector Fields,Workflow,Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control; Data Sampling & Resolution Control,4
+rti-velocity_slices,Physics,Vector Fields,Workflow,Data Subsetting & Extraction; Color & Opacity Mapping; View & Camera Control,3
+rti-velocity_streakline,Physics,Vector Fields; Time-varying,Workflow,Temporal Processing; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control,5
+solar-plume,Astronomy,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+supernova_isosurface,Astronomy,Scalar Fields,Workflow,Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control; Data Subsetting & Extraction,4
+supernova_streamline,Astronomy,Vector Fields,Workflow,Temporal Processing; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control,5
+tangaroa_streamribbon,Physics,Vector Fields,Workflow,Temporal Processing; Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control,4
+tgc-velocity_contour,Physics,Vector Fields,Workflow,Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control; Field Computation,4
+tornado,Earth System Science,Vector Fields,Workflow,Temporal Processing; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control,5
+twoswirls_streamribbon,Physics,Vector Fields,Workflow,Temporal Processing; Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control,4
+vortex,Physics,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+line-plot,Others,Scalar Fields,Task,Plot & Chart Generation; View & Camera Control,2
+ml-dvr,Others,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping; View & Camera Control,3
+ml-iso,Others,Scalar Fields,Task,Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control,3
+ml-slice-iso,Others,Scalar Fields,Task,Data Subsetting & Extraction; Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control,4
+points-surf-clip,Others,Scalar Fields,Task,Data Subsetting & Extraction; Surface & Contour Extraction; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control,5
+shrink-sphere,Others,Scalar Fields,Task,Geometric & Topological Transformation; Color & Opacity Mapping; View & Camera Control,3
+stream-glyph,Others,Vector Fields,Task,Temporal Processing; Glyph & Marker Placement; Color & Opacity Mapping; View & Camera Control,4
+time-varying,Others,Scalar Fields; Time-varying,Task,Temporal Processing; Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control,4
+chart-opacity,Others,Scalar Fields,Workflow,Volume Rendering; Color & Opacity Mapping; View & Camera Control; Plot & Chart Generation,4
+color-blocks,Others,Scalar Fields,Workflow,Color & Opacity Mapping; View & Camera Control,2
+color-data,Others,Scalar Fields,Workflow,Color & Opacity Mapping; View & Camera Control,2
+export-gltf,Others,Scalar Fields,Workflow,Surface & Contour Extraction; Dataset Restructuring; View & Camera Control,3
+import-gltf,Others,Scalar Fields,Workflow,Dataset Restructuring; Color & Opacity Mapping; View & Camera Control,3
+render-histogram,Others,Scalar Fields,Workflow,Plot & Chart Generation; Color & Opacity Mapping; View & Camera Control,3
+reset-camera-direction,Others,Scalar Fields,Task,View & Camera Control; Surface & Contour Extraction,2
+Unknown,Others,Scalar Fields,Task,View & Camera Control,1
+subseries-of-time-series,Others,Scalar Fields; Time-varying,Workflow,Temporal Processing; Volume Rendering; Color & Opacity Mapping; View & Camera Control,4
+write-ply,Others,Scalar Fields,Workflow,Surface & Contour Extraction; Dataset Restructuring; View & Camera Control,3
+climate,Earth System Science,Scalar Fields,Workflow,Surface & Contour Extraction; Color & Opacity Mapping; View & Camera Control,3
+materials,Others,Scalar Fields,Workflow,Color & Opacity Mapping; View & Camera Control,2

statistics/sheets/SciVisAgentBench_Statistics - sci_volume_data.csv

@@ -0,0 +1,35 @@
+Case Name,Application,Data,Task Level 1: Complexity Level,Task Level 2: Visualization Operations,Operation Count
+aneurism,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+christmas_tree,Others,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+hydrogen_atom,Physics,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+silicium,Physics,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+tornado,Physics,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+backpack,Others,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+lobster,Biology,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+neocortical_layer_1_axons,Biology,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+skull,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+vertebra,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+blunt_fin,Physics,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+marmoset_neurons,Biology,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+nucleon,Physics,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+statue_leg,Others,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+vis_male,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+bonsai,Biology,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+engine,Others,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+pancreas,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+stent,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+zeiss,Others,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+boston_teapot,Others,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+foot,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+mri_ventricles,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+present,Others,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+supernova,Astronomy,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+bunny,Others,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+frog,Biology,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+mri_woman,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+prone,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+carp,Biology,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+fuel,Physics,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+mrt_angio,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+shockwave,Physics,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2
+tooth,Medical Science,Scalar Fields,Task,Volume Rendering; Color & Opacity Mapping,2

statistics/sheets/SciVisAgentBench_Statistics - topology.csv

@@ -0,0 +1,10 @@
+Case Name,Application,Data,Task Level 1: Complexity Level,Task Level 2: Visualization Operations,Operation Count
+QMCPACK,Physics; Mathematics,Scalar Fields,Workflow,Field Computation; Feature Identification & Segmentation; Scientific Insight Derivation,3
+brain,Medical Science; Mathematics,Tensor Fields,Workflow,Field Computation; Feature Identification & Segmentation; Scientific Insight Derivation,3
+cylinder,Physics; Mathematics,Scalar Fields,Workflow,Field Computation; Feature Identification & Segmentation; Data Smoothing & Filtering; Scientific Insight Derivation,4
+isabel,Earth System Science; Mathematics,Scalar Fields,Workflow,Field Computation; Feature Identification & Segmentation; Data Smoothing & Filtering; Scientific Insight Derivation,4
+ocean,Earth System Science; Mathematics,Tensor Fields,Workflow,Field Computation; Feature Identification & Segmentation; Scientific Insight Derivation,3
+noisyTerrain,Mathematics,Scalar Fields,Workflow,Field Computation; Feature Identification & Segmentation; Data Smoothing & Filtering,3
+molecule,Chemistry; Mathematics,Scalar Fields,Workflow,Field Computation; Feature Identification & Segmentation,2
+moons,Mathematics,Scalar Fields,Workflow,Field Computation; Feature Identification & Segmentation; Data Smoothing & Filtering,3
+dragon,Mathematics,Scalar Fields,Workflow,Field Computation; Feature Identification & Segmentation,2

statistics/statistics_report.md

@@ -0,0 +1,185 @@
+# SciVisAgentBench - Comprehensive Statistics Report
+*Generated from 5 CSV files in the benchmark*
+---
+## 1. Total Cases Count
+**Important**: Cases = Tasks + Workflows only (Operation-level entries are NOT counted as cases)
+
+### Overall Summary
+- **Total Tasks**: 68
+- **Total Workflows**: 53
+- **Total Cases**: **121** (Tasks + Workflows)
+
+### Breakdown by File
+| File | Tasks | Workflows | Cases (Task+Workflow) | Total Operations |
+|------|-------|-----------|----------------------|-----------------|
+| bioimage_data | 11 | 4 | **15** | 39 |
+| molecular_vis | 13 | 2 | **15** | 45 |
+| sci_volume_data | 34 | 0 | **34** | 68 |
+| topology | 0 | 9 | **9** | 27 |
+| paraview | 10 | 38 | **48** | 169 |
+| **TOTAL** | **68** | **53** | **121** | **348** |
+
+## 2. Application Domain Statistics
+**Note**: These statistics include ONLY cases (Tasks + Workflows). Operation-level entries are excluded.
+
+### Individual Application Counts
+*(Counts individual tags, so multi-tagged entries contribute to multiple categories)*
+
+| Application | Count |
+|-------------|-------|
+| Biology | 28 |
+| Others | 27 |
+| Physics | 26 |
+| Chemistry | 17 |
+| Medical Science | 14 |
+| Mathematics | 9 |
+| Astronomy | 4 |
+| Earth System Science | 4 |
+
+**Total individual application tags**: 129
+
+### Application Combinations
+*(Shows exact combinations as they appear in the data)*
+
+| Application Combination | Count |
+|------------------------|-------|
+| Others | 27 |
+| Biology | 26 |
+| Physics | 24 |
+| Chemistry | 14 |
+| Medical Science | 13 |
+| Astronomy | 4 |
+| Mathematics | 3 |
+| Chemistry; Biology | 2 |
+| Physics; Mathematics | 2 |
+| Earth System Science; Mathematics | 2 |
+| Earth System Science | 2 |
+| Medical Science; Mathematics | 1 |
+| Chemistry; Mathematics | 1 |
+
+## 3. Data Type Statistics
+**Note**: These statistics include ONLY cases (Tasks + Workflows). Operation-level entries are excluded.
+
+### Individual Data Type Counts
+*(Counts individual tags, so multi-tagged entries contribute to multiple categories)*
+
+| Data Type | Count |
+|-----------|-------|
+| Scalar Fields | 74 |
+| Time-varying | 16 |
+| Vector Fields | 16 |
+| Scalar Field | 15 |
+| Multivariate | 15 |
+| Multi-variate | 15 |
+| Tensor Fields | 2 |
+
+**Total individual data type tags**: 153
+
+### Data Type Combinations
+*(Shows exact combinations as they appear in the data)*
+
+| Data Type Combination | Count |
+|-----------------------|-------|
+| Scalar Fields | 71 |
+| Multi-variate | 15 |
+| Vector Fields | 12 |
+| Scalar Field; Multivariate; Time-varying | 11 |
+| Scalar Field; Multivariate | 4 |
+| Vector Fields; Time-varying | 3 |
+| Tensor Fields | 2 |
+| Scalar Fields; Time-varying | 2 |
+| Scalar Fields; Vector Fields | 1 |
+
+## 4. Task Level 1: Complexity Level Statistics
+
+### Overall Distribution
+| Complexity Level | Entry Count | Counted as Case? |
+|------------------|-------------|------------------|
+| Task | 68 | ✅ YES |
+| Workflow | 53 | ✅ YES |
+| **Total Cases** | **121** | **(Tasks + Workflows)** |
+
+## 5. Task Level 2: Visualization Operations Statistics
+**Note**: These statistics include ONLY cases (Tasks + Workflows). Operation-level entries are excluded.
+
+### All Visualization Operations (Sorted by Frequency)
+| Rank | Visualization Operation | Total Count |
+|------|------------------------|-------------|
+| 1 | Color & Opacity Mapping | 95 |
+| 2 | View & Camera Control | 57 |
+| 3 | Volume Rendering | 51 |
+| 4 | Surface & Contour Extraction | 30 |
+| 5 | Data Subsetting & Extraction | 20 |
+| 6 | Field Computation | 19 |
+| 7 | Glyph & Marker Placement | 19 |
+| 8 | Temporal Processing | 17 |
+| 9 | Dataset Restructuring | 10 |
+| 10 | Feature Identification & Segmentation | 10 |
+| 11 | Data Smoothing & Filtering | 7 |
+| 12 | Scientific Insight Derivation | 7 |
+| 13 | Plot & Chart Generation | 3 |
+| 14 | Data Sampling & Resolution Control | 2 |
+| 15 | Geometric & Topological Transformation | 1 |
+
+**Total visualization operation tags**: 348
+
+### Top 10 Most Common Visualization Operations
+| Rank | Operation | Count |
+|------|-----------|-------|
+| 1 | Color & Opacity Mapping | 95 |
+| 2 | View & Camera Control | 57 |
+| 3 | Volume Rendering | 51 |
+| 4 | Surface & Contour Extraction | 30 |
+| 5 | Data Subsetting & Extraction | 20 |
+| 6 | Field Computation | 19 |
+| 7 | Glyph & Marker Placement | 19 |
+| 8 | Temporal Processing | 17 |
+| 9 | Dataset Restructuring | 10 |
+| 10 | Feature Identification & Segmentation | 10 |
+
+### Visualization Operations by Complexity Level (Cases Only)
+
+#### Task Level (Top 10)
+| Rank | Operation | Count |
+|------|-----------|-------|
+| 1 | Color & Opacity Mapping | 54 |
+| 2 | Volume Rendering | 36 |
+| 3 | Data Subsetting & Extraction | 14 |
+| 4 | View & Camera Control | 14 |
+| 5 | Surface & Contour Extraction | 10 |
+| 6 | Glyph & Marker Placement | 7 |
+| 7 | Field Computation | 6 |
+| 8 | Dataset Restructuring | 5 |
+| 9 | Temporal Processing | 3 |
+| 10 | Data Smoothing & Filtering | 1 |
+
+#### Workflow Level (Top 10)
+| Rank | Operation | Count |
+|------|-----------|-------|
+| 1 | View & Camera Control | 43 |
+| 2 | Color & Opacity Mapping | 41 |
+| 3 | Surface & Contour Extraction | 20 |
+| 4 | Volume Rendering | 15 |
+| 5 | Temporal Processing | 14 |
+| 6 | Field Computation | 13 |
+| 7 | Glyph & Marker Placement | 12 |
+| 8 | Feature Identification & Segmentation | 10 |
+| 9 | Scientific Insight Derivation | 7 |
+| 10 | Data Subsetting & Extraction | 6 |
+
+## 6. Summary Statistics
+- **Total number of CSV files analyzed**: 5
+- **Total Cases (Tasks + Workflows)**: **121**
+- **Unique application domains**: 8
+- **Unique data types**: 7
+- **Unique visualization operations**: 15
+
+### File Contributions
+| File | Cases Contributed | Percentage |
+|------|-------------------|------------|
+| paraview | 48 | 39.7% |
+| sci_volume_data | 34 | 28.1% |
+| bioimage_data | 15 | 12.4% |
+| molecular_vis | 15 | 12.4% |
+| topology | 9 | 7.4% |
+

statistics/taxonomy.md

@@ -0,0 +1,181 @@
+# Application Taxonomy
+
+- Astronomy
+- Medical Science
+- Biology
+- Physics
+- Earth System Science
+- Mathematics
+- Chemistry
+- Others
+
+
+
+
+# Data Type Taxonomy
+
+- Scalar Fields
+- Vector Fields
+- Tensor Fields
+- Multivariate
+- Time-varying
+
+
+# Task Level 1: Complexity Level Taxonomy
+
+- **Operation** - Single, focused visualization operation
+- **Task** - Small-scale visualization task involving a few operations
+- **Workflow** - Multi-step visualization pipeline or complex process
+
+
+# Task Level 2: Visualization Operations Taxonomy
+
+Each task in SciVisAgentBench is tagged with one or more of the following operations. These tags describe **what visualization or data processing actions** are performed.
+
+---
+
+## 1. Data Subsetting & Extraction
+
+**Brief:** Isolate spatial regions or value-based subsets from a dataset.
+
+**Detailed:** This operation covers methods of filtering data to focus on a region or set of values of interest. It includes spatial subsetting such as clipping by plane, box, or sphere; extracting a volume of interest (VOI) from structured data; selecting by point or cell IDs; thresholding to filter by scalar range; isocontouring or isosurfacing to extract constant-value boundaries; and connectivity filtering to isolate connected components. The common thread is that the operation reduces the dataset to a subset defined by spatial location or attribute value.
+
+---
+
+## 2. Data Sampling & Resolution Control
+
+**Brief:** Modify data density or sampling resolution for efficiency or clarity.
+
+**Detailed:** This operation covers methods that change how densely data is represented while preserving its overall semantics. It includes subsampling and decimation to reduce point or cell count, resampling onto different grids, probing fields at discrete user-specified locations, and generating reduced representations for performance or visual clarity. Unlike Data Subsetting & Extraction (which selects regions of interest), this operation controls the fidelity or granularity of the entire dataset or a uniformly sampled portion of it.
+
+---
+
+## 3. Surface & Contour Extraction
+
+**Brief:** Generate isosurfaces, contour lines, or cut-plane slices from volumetric data.
+Generate isosurfaces, contour lines, ribbons, or tubes from volumetric data.
+
+**Detailed:** This operation covers the extraction of geometric primitives that represent boundaries or cross-sections within a volume. It includes isosurface generation at user-specified scalar thresholds, contour line or contour surface extraction, slicing or cutting along a plane to produce a 2D cross-section, and generation of ribbons, tubes, or streamline geometry from curve data. Unlike Data Subsetting & Extraction (which filters data), this operation creates new geometry that represents a specific feature boundary or spatial cross-section of the original volume.
+
+---
+
+## 4. Volume Rendering
+
+**Brief:** Render volumetric data directly using ray casting or splatting.
+
+**Detailed:** This operation covers techniques that visualize an entire 3D volume without reducing it to a surface. It includes ray-casting volume rendering, point splatting, and the definition or adjustment of transfer functions that map scalar values to color and opacity. Multi-channel volume rendering with additive blending or X-ray style transparency also falls here. The key characteristic is that the full volume participates in the visual output, as opposed to extracting a surface or slice.
+
+---
+
+## 5. Glyph & Marker Placement
+
+**Brief:** Place oriented, scaled, or typed glyphs at data points to encode local attributes.
+
+**Detailed:** This operation covers the generation and placement of glyphs at sample locations to encode one or more data attributes visually. It includes oriented glyphs such as arrows or cones for vector direction, scaled glyphs where symbol size maps to magnitude, tensor glyphs such as ellipsoids or superquadrics, and volume markers or seed points. It also covers molecular representation types — spheres (van der Waals), sticks (licorice), cartoons (backbone), and surfaces — which are all glyph-style encodings of atomic or residue-level data. The defining feature is that discrete symbols are placed at specific locations to represent local data values.
+
+---
+
+## 6. Color & Opacity Mapping
+
+**Brief:** Assign colors, opacity, or textures to data elements based on attribute values.
+
+**Detailed:** This operation covers all mappings from data attributes to visual properties. It includes scalar-to-color mapping using colormaps (e.g., viridis, rainbow, grayscale), opacity or transparency mapping across a scalar range, texture coordinate generation and texture application, material property assignment such as shininess or ambient light, and contrast or gamma adjustments for display. In molecular visualization, coloring by element, chain, residue type, B-factor, charge, or hydrophobicity are all instances of this operation. The key distinction from Volume Rendering or Glyph Placement is that this operation defines the visual encoding rule, not the geometry.
+
+---
+
+## 7. Geometric & Topological Transformation
+
+**Brief:** Modify the geometry or connectivity structure of a dataset.
+
+**Detailed:** This operation covers transformations that change the shape, position, or topological structure of data without computing new scalar or vector attributes. Geometric transforms include translation, rotation, scaling, deformation, and warping of vertices. Topological transforms include triangulation or tessellation of polygons, mesh refinement or coarsening, cell-type conversion, and boundary extraction (deriving a surface mesh from a volume). These operations alter how the data is structured spatially but do not derive new field values.
+
+---
+
+## 8. Dataset Restructuring
+
+**Brief:** Combine, partition, or reorganize multiple datasets or data blocks.
+
+**Detailed:** This operation covers structural changes to how datasets are organized at the block or file level, rather than modifying geometry or attributes. It includes merging two or more datasets into a single output, appending new data blocks, splitting or partitioning a dataset along boundaries, and format or type conversion between file structures (e.g., structured to unstructured grid). The defining characteristic is that the operation reorganizes data provenance or container structure rather than transforming geometry or computing attributes.
+
+---
+
+## 9. Field Computation
+
+**Brief:** Derive new scalar, vector, or tensor fields from existing data attributes.
+
+**Detailed:** This operation covers all calculations that produce new attribute arrays from existing ones. It includes gradient, divergence, curl, and vorticity computation; curvature calculation and normal generation; arithmetic operations on fields such as addition, subtraction, and multiplication; vector magnitude computation and component extraction; field aggregation and statistical summaries (min, max, mean, standard deviation); tensor operations such as eigenvalue and eigenvector decomposition; interpolation between fields; and distance computations. Critical point detection also falls here, as it derives point locations and classifications from scalar field topology.
+
+---
+
+## 10. Temporal Processing
+
+**Brief:** Perform computations or navigation that involve the time dimension of data.
+
+**Detailed:** This operation covers all actions that explicitly use or traverse the temporal axis of time-varying data. It includes temporal interpolation between timesteps, particle tracing or trajectory computation through time, flow integration to produce streamlines, pathlines, streaklines, or timelines, temporal statistics or aggregation across multiple timesteps, flow map computation, and navigating or selecting specific timesteps in a time series. The defining feature is that time is an active dimension in the operation, distinguishing it from static spatial analysis.
+
+---
+
+## 11. Feature Identification & Segmentation (Or Feature Extraction & Tracking?)
+
+**Brief:** Detect, extract, or label discrete structures or regions in data.
+
+**Detailed:** This operation covers computational methods that identify and classify meaningful structures within a dataset. It includes connected component labeling, object detection and identification, region segmentation, feature extraction such as vortices or boundaries, topology-based feature classification (e.g., critical point types), and tissue or structure classification from intensity distributions. The output is a set of labeled objects or regions — a computational result — as distinct from Scientific Insight Derivation, which interprets those results to answer a domain question.
+
+---
+
+## 12. Data Smoothing & Filtering
+
+**Brief:** Reduce noise, enhance features, or apply statistical filters to data.
+
+**Detailed:** This operation covers preprocessing steps that modify attribute values to improve quality or visual clarity without changing geometry or topology. It includes surface smoothing algorithms such as Laplacian or Gaussian smoothing, noise reduction on scalar or vector fields, interpolation to fill missing data gaps, edge enhancement or detection, feature sharpening, outlier removal, and statistical filtering. The goal is to condition the data for cleaner analysis or visualization, as distinct from Field Computation which derives fundamentally new quantities.
+
+---
+
+## 13. View & Camera Control
+
+**Brief:** Adjust camera position, orientation, zoom, or lighting for the visualization.
+
+**Detailed:** This operation covers all manipulations of the viewing context rather than the data itself. It includes camera rotation, zoom, and panning; setting specific viewpoints such as isometric, orthographic, or axis-aligned views; adjusting light position and lighting mode; switching between 2D and 3D view modes; and resetting the camera to default. In molecular visualization, this includes centering on a specific residue or region and adjusting the viewing angle to show a particular structural feature.
+
+---
+
+## 14. Plot & Chart Generation
+
+**Brief:** Produce 2D statistical plots, histograms, or line charts from data.
+
+**Detailed:** This operation covers the creation of traditional 2D chart representations from dataset attributes. It includes histogram generation from scalar distributions, line plot extraction, such as intensity profiles or time series, bar charts, and other standard statistical visualizations. This is distinct from Volume Rendering or Surface Extraction because the output is a 2D chart rather than a 3D visual representation of spatial data. It is also distinct from Field Computation because the operation produces a visual artifact (the chart) rather than a new data field.
+
+---
+
+## 15. Scientific Insight Derivation
+
+**Brief:** Interpret visualization or analysis results to answer domain-specific questions.
+
+**Detailed:** This operation covers tasks where the goal is not to produce a particular visual output or labeled data, but to use visualization or analysis to answer a scientific question or derive a domain-relevant conclusion. Examples include determining whether water molecules penetrate a lipid membrane, assessing whether a rendered volume shows a specific anatomical structure, or validating the physical plausibility of simulation results. The defining characteristic is that the output is knowledge or a decision, not a visualization or data artifact. This operation frequently co-occurs with Feature Identification & Segmentation or other operations that provide the analytical basis for the insight.
+
+---
+
+## Mapping from Previous Tags
+
+| Previous Tag | New Tag(s) |
+|---|---|
+| Spatial/temporal Extraction | Data Subsetting & Extraction |
+| Value-Based Selection | Data Subsetting & Extraction |
+| Sampling | Data Sampling & Resolution Control |
+| Geometric Primitives | Surface & Contour Extraction |
+| Volume Representation | Volume Rendering |
+| Glyph-Based Representation | Glyph & Marker Placement |
+| Color Mapping | Color & Opacity Mapping |
+| Geometric Modification | Geometric & Topological Transform |
+| Topological Changes | Geometric & Topological Transform |
+| Structural Operations | Dataset Restructuring |
+| Field Derivatives | Field Computation |
+| Scalar Operations | Field Computation |
+| Advanced Computations | Field Computation |
+| Time-Dependent Processing | Temporal Processing |
+| Object identification | Feature Identification & Segmentation |
+| Smoothing & enhancement | Data Smoothing & Filtering |
+| View / Rendering Manipulation | View & Camera Control |
+| Plot Drawing | Plot & Chart Generation |
+
+