Version history

{
  "content_md": "# Rich Artifacts\n\nArtifacts are generated research outputs that enrich hypotheses and analyses with inspectable evidence. They range from figures and notebooks to protein structures and knowledge graph edges.\n\n## Pathway Diagram\n\n\nflowchart TD\n    N0[\"PER\"]\n    N1[\"Als\"]\n    N0 -->|\"associated with\"| N1\n    N0 -->|\"activates\"| N1\n    N2[\"Cancer\"]\n    N0 -->|\"expressed in\"| N2\n    N2 -->|\"expressed in\"| N0\n    N3[\"Neurodegeneration\"]\n    N0 -->|\"associated with\"| N3\n    N0 -->|\"associated with\"| N2\n    N4[\"Stroke\"]\n    N0 -->|\"associated with\"| N4\n    N0 -->|\"expressed in\"| N1\n    N5[\"METABOLIC HEALTH\"]\n    N0 -->|\"regulates\"| N5\n    N6[\"circadian rhythm\"]\n    N0 -->|\"drives\"| N6\n    N7[\"Circadian Rhythms\"]\n    N0 -->|\"involved in\"| N7\n    N8[\"Cellular Metabolism\"]\n    N0 -->|\"regulates\"| N8\n\n## Why Artifacts?\n\nScientific claims need more than text — they need data, visualizations, and computational evidence. Artifacts make SciDEX discoveries:\n- **Transparent** — See the data behind the claim\n- **Reproducible** — Access the code and methodology\n- **Inspectable** — Drill down into details\n- **Citable** — Each artifact has a permanent ID\n\n## Artifact Types\n\nSciDEX tracks **37,664 artifacts** across 17 types:\n\n| Type | Count | Description |\n|------|-------|-------------|\n| `wiki_page` | 18,434 | Wiki page exports and summaries |\n| `figure` | 9,603 | Generated charts, plots, and diagrams |\n| `kg_edge` | 7,618 | Knowledge graph relationship evidence |\n| `experiment` | 631 | Computational experiments |\n| `paper` | 520 | Scientific papers and preprints |\n| `notebook` | 233 | Jupyter computational notebooks |\n| `paper_figure` | 216 | Extracted figures from literature |\n| `hypothesis` | 200 | Hypothesis snapshots with provenance |\n| `analysis` | 105 | Structured analysis reports |\n| `protein_design` | 21 | Protein structure predictions |\n| `model` | 7 | Computational models |\n| `dataset` | 6 | Curated datasets |\n| `dashboard` | 5 | Interactive data dashboards |\n| `tabular_dataset` | 3 | Structured tabular data |\n| `ai_image` | 4 | AI-generated scientific images |\n| `authored_paper` | 2 | Full paper summaries |\n| `code` | 1 | Source code artifacts |\n\n## Artifact Registry\n\nAll artifacts are tracked in the `artifacts` table with:\n- **Unique ID** — Permanent reference\n- **Type** — Classification (notebook, figure, etc.)\n- **Parent IDs** — What hypothesis/analysis/entity it supports\n- **Metadata** — Tags, quality score, visibility\n- **Storage** — File path or database reference\n\n## Protein Structure Visualization (Quest 17)\n\nOne of SciDEX's most powerful artifact types is **interactive 3D protein structure viewers** embedded directly in hypothesis and entity pages. Using the Mol* viewer (the same technology powering RCSB PDB and AlphaFold), users can explore protein structures mentioned in scientific claims.\n\n### How Protein Viewers Work\n\nSciDEX uses a three-tier fallback strategy to maximize structure coverage:\n\n#### 1. PDB Experimental Structures (Preferred)\n\nWhen available, SciDEX displays high-resolution experimental structures from the RCSB Protein Data Bank:\n- X-ray crystallography structures\n- Cryo-EM electron microscopy maps\n- NMR solution structures\n\n**Example genes with PDB structures:** TREM2, MAPT, SNCA, APP, PSEN1\n\n#### 2. AlphaFold Predicted Structures (Fallback)\n\nFor human proteins without experimental structures, SciDEX uses AlphaFold predictions via UniProt IDs:\n- AI-predicted structures with per-residue confidence scores\n- Covers most of the human proteome\n- Useful for intrinsically disordered regions and novel proteins\n\n#### 3. Dynamic PDB Search (Fallback)\n\nIf no pre-mapped structure exists, SciDEX queries the RCSB PDB API in real-time:\n- Live search by gene name\n- Returns best-matching structure\n- Graceful degradation to external links if no structure found\n\n### Viewer Features\n\nInteractive controls for all viewers:\n- **Rotate:** Click and drag\n- **Zoom:** Mouse scroll\n- **Reset:** Right-click\n- **Lazy-loading:** Viewers only render when expanded (performance optimization)\n- **External links:** Direct links to PDB/AlphaFold source pages\n\n### Where to Find Protein Viewers\n\n- **Hypothesis pages:** Automatically embedded when `target_gene` is specified\n- **Entity pages:** Protein entity pages show all available structures\n- **Drug target pages:** Therapeutic target pages highlight binding sites and domains\n\n**Current coverage:** 50+ proteins with interactive 3D viewers across demo hypotheses and high-priority entities.\n\n## Using Artifacts\n\n### On Hypothesis Pages\nArtifacts appear in dedicated sections:\n- \"Evidence Visualizations\" for figures\n- \"Computational Analysis\" for notebooks\n- \"Supporting Literature\" for paper figures\n\n### In the Artifact Gallery\nBrowse all artifacts at `/artifacts`:\n- Filter by type\n- Search by keyword\n- Sort by quality or recency\n\n### Via API\nProgrammatic access via `/api/artifacts`:\n```\nGET /api/artifacts?type=notebook&parent_type=hypothesis&parent_id=abc123\n```\n\n## Artifact Quality\n\nNot all artifacts are equal. Quality indicators:\n- **Confidence score** — System-assessed reliability\n- **Citation count** — How often referenced\n- **Review status** — Manual curation for high-visibility pages\n\nDemo pages feature only high-quality, reviewed artifacts.\n\n## Creating Artifacts\n\n- **Forge agents** generate most artifacts during analysis workflows\n- **Human contributors** can submit via `/join` registration\n- **Quality gates** ensure artifacts meet standards before publication\n\n**See also:** [Notebooks](/docs/notebooks), [Forge Layer](/docs/five-layers)\n\n## Pathway Diagram\n\nThe following diagram shows the key molecular relationships involving Rich Artifacts discovered through SciDEX knowledge graph analysis:\n\n```mermaid\ngraph TD\n    CANCER[\"CANCER\"] -->|\"expressed in\"| PER[\"PER\"]\n    CANCER[\"CANCER\"] -->|\"associated with\"| PER[\"PER\"]\n    OXIDATIVE_STRESS[\"OXIDATIVE STRESS\"] -->|\"activates\"| PER[\"PER\"]\n    GENES[\"GENES\"] -->|\"associated with\"| PER[\"PER\"]\n    OBSTRUCTIVE_SLEEP_APNEA_SYNDRO[\"OBSTRUCTIVE SLEEP APNEA SYNDROME\"] -->|\"modulates\"| PER[\"PER\"]\n    DNA[\"DNA\"] -->|\"associated with\"| PER[\"PER\"]\n    STROKE[\"STROKE\"] -->|\"associated with\"| PER[\"PER\"]\n    NEURODEGENERATION[\"NEURODEGENERATION\"] -->|\"associated with\"| PER[\"PER\"]\n    ALZHEIMER[\"ALZHEIMER\"] -->|\"associated with\"| PER[\"PER\"]\n    Obstructive_Sleep_Apnea_Syndro[\"Obstructive Sleep Apnea Syndrome\"] -->|\"modulates\"| PER[\"PER\"]\n    OSAS[\"OSAS\"] -->|\"modulates\"| PER[\"PER\"]\n    CANCER[\"CANCER\"] -->|\"activates\"| PER[\"PER\"]\n    MITOCHONDRIAL_DNA[\"MITOCHONDRIAL DNA\"] -->|\"associated with\"| PER[\"PER\"]\n    DNA[\"DNA\"] -->|\"expressed in\"| PER[\"PER\"]\n    NEURODEGENERATIVE_DISEASES[\"NEURODEGENERATIVE DISEASES\"] -->|\"regulates\"| PER[\"PER\"]\n    style CANCER fill:#ce93d8,stroke:#333,color:#000\n    style PER fill:#ce93d8,stroke:#333,color:#000\n    style OXIDATIVE_STRESS fill:#ce93d8,stroke:#333,color:#000\n    style GENES fill:#ce93d8,stroke:#333,color:#000\n    style OBSTRUCTIVE_SLEEP_APNEA_SYNDRO fill:#4fc3f7,stroke:#333,color:#000\n    style DNA fill:#ce93d8,stroke:#333,color:#000\n    style STROKE fill:#ce93d8,stroke:#333,color:#000\n    style NEURODEGENERATION fill:#ce93d8,stroke:#333,color:#000\n    style ALZHEIMER fill:#ce93d8,stroke:#333,color:#000\n    style Obstructive_Sleep_Apnea_Syndro fill:#ef5350,stroke:#333,color:#000\n    style OSAS fill:#ef5350,stroke:#333,color:#000\n    style MITOCHONDRIAL_DNA fill:#ce93d8,stroke:#333,color:#000\n    style NEURODEGENERATIVE_DISEASES fill:#ce93d8,stroke:#333,color:#000\n```\n\n",
  "entity_type": "scidex_docs"
}

{
  "content_md": "# Rich Artifacts\n\nArtifacts are generated research outputs that enrich hypotheses and analyses with inspectable evidence. They range from figures and notebooks to protein structures and knowledge graph edges.\n\n## Pathway Diagram\n\n\n```mermaid\nflowchart TD\n    N0[\"PER\"]\n    N1[\"Als\"]\n    N0 -->|\"associated with\"| N1\n    N0 -->|\"activates\"| N1\n    N2[\"Cancer\"]\n    N0 -->|\"expressed in\"| N2\n    N2 -->|\"expressed in\"| N0\n    N3[\"Neurodegeneration\"]\n    N0 -->|\"associated with\"| N3\n    N0 -->|\"associated with\"| N2\n    N4[\"Stroke\"]\n    N0 -->|\"associated with\"| N4\n    N0 -->|\"expressed in\"| N1\n    N5[\"METABOLIC HEALTH\"]\n    N0 -->|\"regulates\"| N5\n    N6[\"circadian rhythm\"]\n    N0 -->|\"drives\"| N6\n    N7[\"Circadian Rhythms\"]\n    N0 -->|\"involved in\"| N7\n    N8[\"Cellular Metabolism\"]\n    N0 -->|\"regulates\"| N8\n```\n\n## Why Artifacts?\n\nScientific claims need more than text — they need data, visualizations, and computational evidence. Artifacts make SciDEX discoveries:\n- **Transparent** — See the data behind the claim\n- **Reproducible** — Access the code and methodology\n- **Inspectable** — Drill down into details\n- **Citable** — Each artifact has a permanent ID\n\n## Artifact Types\n\nSciDEX tracks **37,664 artifacts** across 17 types:\n\n| Type | Count | Description |\n|------|-------|-------------|\n| `wiki_page` | 18,434 | Wiki page exports and summaries |\n| `figure` | 9,603 | Generated charts, plots, and diagrams |\n| `kg_edge` | 7,618 | Knowledge graph relationship evidence |\n| `experiment` | 631 | Computational experiments |\n| `paper` | 520 | Scientific papers and preprints |\n| `notebook` | 233 | Jupyter computational notebooks |\n| `paper_figure` | 216 | Extracted figures from literature |\n| `hypothesis` | 200 | Hypothesis snapshots with provenance |\n| `analysis` | 105 | Structured analysis reports |\n| `protein_design` | 21 | Protein structure predictions |\n| `model` | 7 | Computational models |\n| `dataset` | 6 | Curated datasets |\n| `dashboard` | 5 | Interactive data dashboards |\n| `tabular_dataset` | 3 | Structured tabular data |\n| `ai_image` | 4 | AI-generated scientific images |\n| `authored_paper` | 2 | Full paper summaries |\n| `code` | 1 | Source code artifacts |\n\n## Artifact Registry\n\nAll artifacts are tracked in the `artifacts` table with:\n- **Unique ID** — Permanent reference\n- **Type** — Classification (notebook, figure, etc.)\n- **Parent IDs** — What hypothesis/analysis/entity it supports\n- **Metadata** — Tags, quality score, visibility\n- **Storage** — File path or database reference\n\n## Protein Structure Visualization (Quest 17)\n\nOne of SciDEX's most powerful artifact types is **interactive 3D protein structure viewers** embedded directly in hypothesis and entity pages. Using the Mol* viewer (the same technology powering RCSB PDB and AlphaFold), users can explore protein structures mentioned in scientific claims.\n\n### How Protein Viewers Work\n\nSciDEX uses a three-tier fallback strategy to maximize structure coverage:\n\n#### 1. PDB Experimental Structures (Preferred)\n\nWhen available, SciDEX displays high-resolution experimental structures from the RCSB Protein Data Bank:\n- X-ray crystallography structures\n- Cryo-EM electron microscopy maps\n- NMR solution structures\n\n**Example genes with PDB structures:** TREM2, MAPT, SNCA, APP, PSEN1\n\n#### 2. AlphaFold Predicted Structures (Fallback)\n\nFor human proteins without experimental structures, SciDEX uses AlphaFold predictions via UniProt IDs:\n- AI-predicted structures with per-residue confidence scores\n- Covers most of the human proteome\n- Useful for intrinsically disordered regions and novel proteins\n\n#### 3. Dynamic PDB Search (Fallback)\n\nIf no pre-mapped structure exists, SciDEX queries the RCSB PDB API in real-time:\n- Live search by gene name\n- Returns best-matching structure\n- Graceful degradation to external links if no structure found\n\n### Viewer Features\n\nInteractive controls for all viewers:\n- **Rotate:** Click and drag\n- **Zoom:** Mouse scroll\n- **Reset:** Right-click\n- **Lazy-loading:** Viewers only render when expanded (performance optimization)\n- **External links:** Direct links to PDB/AlphaFold source pages\n\n### Where to Find Protein Viewers\n\n- **Hypothesis pages:** Automatically embedded when `target_gene` is specified\n- **Entity pages:** Protein entity pages show all available structures\n- **Drug target pages:** Therapeutic target pages highlight binding sites and domains\n\n**Current coverage:** 50+ proteins with interactive 3D viewers across demo hypotheses and high-priority entities.\n\n## Using Artifacts\n\n### On Hypothesis Pages\nArtifacts appear in dedicated sections:\n- \"Evidence Visualizations\" for figures\n- \"Computational Analysis\" for notebooks\n- \"Supporting Literature\" for paper figures\n\n### In the Artifact Gallery\nBrowse all artifacts at `/artifacts`:\n- Filter by type\n- Search by keyword\n- Sort by quality or recency\n\n### Via API\nProgrammatic access via `/api/artifacts`:\n```\nGET /api/artifacts?type=notebook&parent_type=hypothesis&parent_id=abc123\n```\n\n## Artifact Quality\n\nNot all artifacts are equal. Quality indicators:\n- **Confidence score** — System-assessed reliability\n- **Citation count** — How often referenced\n- **Review status** — Manual curation for high-visibility pages\n\nDemo pages feature only high-quality, reviewed artifacts.\n\n## Creating Artifacts\n\n- **Forge agents** generate most artifacts during analysis workflows\n- **Human contributors** can submit via `/join` registration\n- **Quality gates** ensure artifacts meet standards before publication\n\n**See also:** [Notebooks](/docs/notebooks), [Forge Layer](/docs/five-layers)\n\n## Pathway Diagram\n\nThe following diagram shows the key molecular relationships involving Rich Artifacts discovered through SciDEX knowledge graph analysis:\n\n```mermaid\ngraph TD\n    CANCER[\"CANCER\"] -->|\"expressed in\"| PER[\"PER\"]\n    CANCER[\"CANCER\"] -->|\"associated with\"| PER[\"PER\"]\n    OXIDATIVE_STRESS[\"OXIDATIVE STRESS\"] -->|\"activates\"| PER[\"PER\"]\n    GENES[\"GENES\"] -->|\"associated with\"| PER[\"PER\"]\n    OBSTRUCTIVE_SLEEP_APNEA_SYNDRO[\"OBSTRUCTIVE SLEEP APNEA SYNDROME\"] -->|\"modulates\"| PER[\"PER\"]\n    DNA[\"DNA\"] -->|\"associated with\"| PER[\"PER\"]\n    STROKE[\"STROKE\"] -->|\"associated with\"| PER[\"PER\"]\n    NEURODEGENERATION[\"NEURODEGENERATION\"] -->|\"associated with\"| PER[\"PER\"]\n    ALZHEIMER[\"ALZHEIMER\"] -->|\"associated with\"| PER[\"PER\"]\n    Obstructive_Sleep_Apnea_Syndro[\"Obstructive Sleep Apnea Syndrome\"] -->|\"modulates\"| PER[\"PER\"]\n    OSAS[\"OSAS\"] -->|\"modulates\"| PER[\"PER\"]\n    CANCER[\"CANCER\"] -->|\"activates\"| PER[\"PER\"]\n    MITOCHONDRIAL_DNA[\"MITOCHONDRIAL DNA\"] -->|\"associated with\"| PER[\"PER\"]\n    DNA[\"DNA\"] -->|\"expressed in\"| PER[\"PER\"]\n    NEURODEGENERATIVE_DISEASES[\"NEURODEGENERATIVE DISEASES\"] -->|\"regulates\"| PER[\"PER\"]\n    style CANCER fill:#ce93d8,stroke:#333,color:#000\n    style PER fill:#ce93d8,stroke:#333,color:#000\n    style OXIDATIVE_STRESS fill:#ce93d8,stroke:#333,color:#000\n    style GENES fill:#ce93d8,stroke:#333,color:#000\n    style OBSTRUCTIVE_SLEEP_APNEA_SYNDRO fill:#4fc3f7,stroke:#333,color:#000\n    style DNA fill:#ce93d8,stroke:#333,color:#000\n    style STROKE fill:#ce93d8,stroke:#333,color:#000\n    style NEURODEGENERATION fill:#ce93d8,stroke:#333,color:#000\n    style ALZHEIMER fill:#ce93d8,stroke:#333,color:#000\n    style Obstructive_Sleep_Apnea_Syndro fill:#ef5350,stroke:#333,color:#000\n    style OSAS fill:#ef5350,stroke:#333,color:#000\n    style MITOCHONDRIAL_DNA fill:#ce93d8,stroke:#333,color:#000\n    style NEURODEGENERATIVE_DISEASES fill:#ce93d8,stroke:#333,color:#000\n```\n\n",
  "entity_type": "scidex_docs"
}

{
  "content_md": "# Rich Artifacts\n\nArtifacts are generated research outputs that enrich hypotheses and analyses with inspectable evidence. They range from figures and notebooks to protein structures and knowledge graph edges.\n\n## Pathway Diagram\n\n\nflowchart TD\n    N0[\"PER\"]\n    N1[\"Als\"]\n    N0 -->|\"associated with\"| N1\n    N0 -->|\"activates\"| N1\n    N2[\"Cancer\"]\n    N0 -->|\"expressed in\"| N2\n    N2 -->|\"expressed in\"| N0\n    N3[\"Neurodegeneration\"]\n    N0 -->|\"associated with\"| N3\n    N0 -->|\"associated with\"| N2\n    N4[\"Stroke\"]\n    N0 -->|\"associated with\"| N4\n    N0 -->|\"expressed in\"| N1\n    N5[\"METABOLIC HEALTH\"]\n    N0 -->|\"regulates\"| N5\n    N6[\"circadian rhythm\"]\n    N0 -->|\"drives\"| N6\n    N7[\"Circadian Rhythms\"]\n    N0 -->|\"involved in\"| N7\n    N8[\"Cellular Metabolism\"]\n    N0 -->|\"regulates\"| N8\n\n## Why Artifacts?\n\nScientific claims need more than text — they need data, visualizations, and computational evidence. Artifacts make SciDEX discoveries:\n- **Transparent** — See the data behind the claim\n- **Reproducible** — Access the code and methodology\n- **Inspectable** — Drill down into details\n- **Citable** — Each artifact has a permanent ID\n\n## Artifact Types\n\nSciDEX tracks **37,664 artifacts** across 17 types:\n\n| Type | Count | Description |\n|------|-------|-------------|\n| `wiki_page` | 18,434 | Wiki page exports and summaries |\n| `figure` | 9,603 | Generated charts, plots, and diagrams |\n| `kg_edge` | 7,618 | Knowledge graph relationship evidence |\n| `experiment` | 631 | Computational experiments |\n| `paper` | 520 | Scientific papers and preprints |\n| `notebook` | 233 | Jupyter computational notebooks |\n| `paper_figure` | 216 | Extracted figures from literature |\n| `hypothesis` | 200 | Hypothesis snapshots with provenance |\n| `analysis` | 105 | Structured analysis reports |\n| `protein_design` | 21 | Protein structure predictions |\n| `model` | 7 | Computational models |\n| `dataset` | 6 | Curated datasets |\n| `dashboard` | 5 | Interactive data dashboards |\n| `tabular_dataset` | 3 | Structured tabular data |\n| `ai_image` | 4 | AI-generated scientific images |\n| `authored_paper` | 2 | Full paper summaries |\n| `code` | 1 | Source code artifacts |\n\n## Artifact Registry\n\nAll artifacts are tracked in the `artifacts` table with:\n- **Unique ID** — Permanent reference\n- **Type** — Classification (notebook, figure, etc.)\n- **Parent IDs** — What hypothesis/analysis/entity it supports\n- **Metadata** — Tags, quality score, visibility\n- **Storage** — File path or database reference\n\n## Protein Structure Visualization (Quest 17)\n\nOne of SciDEX's most powerful artifact types is **interactive 3D protein structure viewers** embedded directly in hypothesis and entity pages. Using the Mol* viewer (the same technology powering RCSB PDB and AlphaFold), users can explore protein structures mentioned in scientific claims.\n\n### How Protein Viewers Work\n\nSciDEX uses a three-tier fallback strategy to maximize structure coverage:\n\n#### 1. PDB Experimental Structures (Preferred)\n\nWhen available, SciDEX displays high-resolution experimental structures from the RCSB Protein Data Bank:\n- X-ray crystallography structures\n- Cryo-EM electron microscopy maps\n- NMR solution structures\n\n**Example genes with PDB structures:** TREM2, MAPT, SNCA, APP, PSEN1\n\n#### 2. AlphaFold Predicted Structures (Fallback)\n\nFor human proteins without experimental structures, SciDEX uses AlphaFold predictions via UniProt IDs:\n- AI-predicted structures with per-residue confidence scores\n- Covers most of the human proteome\n- Useful for intrinsically disordered regions and novel proteins\n\n#### 3. Dynamic PDB Search (Fallback)\n\nIf no pre-mapped structure exists, SciDEX queries the RCSB PDB API in real-time:\n- Live search by gene name\n- Returns best-matching structure\n- Graceful degradation to external links if no structure found\n\n### Viewer Features\n\nInteractive controls for all viewers:\n- **Rotate:** Click and drag\n- **Zoom:** Mouse scroll\n- **Reset:** Right-click\n- **Lazy-loading:** Viewers only render when expanded (performance optimization)\n- **External links:** Direct links to PDB/AlphaFold source pages\n\n### Where to Find Protein Viewers\n\n- **Hypothesis pages:** Automatically embedded when `target_gene` is specified\n- **Entity pages:** Protein entity pages show all available structures\n- **Drug target pages:** Therapeutic target pages highlight binding sites and domains\n\n**Current coverage:** 50+ proteins with interactive 3D viewers across demo hypotheses and high-priority entities.\n\n## Using Artifacts\n\n### On Hypothesis Pages\nArtifacts appear in dedicated sections:\n- \"Evidence Visualizations\" for figures\n- \"Computational Analysis\" for notebooks\n- \"Supporting Literature\" for paper figures\n\n### In the Artifact Gallery\nBrowse all artifacts at `/artifacts`:\n- Filter by type\n- Search by keyword\n- Sort by quality or recency\n\n### Via API\nProgrammatic access via `/api/artifacts`:\n```\nGET /api/artifacts?type=notebook&parent_type=hypothesis&parent_id=abc123\n```\n\n## Artifact Quality\n\nNot all artifacts are equal. Quality indicators:\n- **Confidence score** — System-assessed reliability\n- **Citation count** — How often referenced\n- **Review status** — Manual curation for high-visibility pages\n\nDemo pages feature only high-quality, reviewed artifacts.\n\n## Creating Artifacts\n\n- **Forge agents** generate most artifacts during analysis workflows\n- **Human contributors** can submit via `/join` registration\n- **Quality gates** ensure artifacts meet standards before publication\n\n**See also:** [Notebooks](/docs/notebooks), [Forge Layer](/docs/five-layers)",
  "entity_type": "scidex_docs"
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{
  "content_md": "# Rich Artifacts\n\nArtifacts are generated research outputs that enrich hypotheses and analyses with inspectable evidence. They range from figures and notebooks to protein structures and knowledge graph edges.\n\n## Pathway Diagram\n\n\n```mermaid\nflowchart TD\n    N0[\"PER\"]\n    N1[\"Als\"]\n    N0 -->|\"associated with\"| N1\n    N0 -->|\"activates\"| N1\n    N2[\"Cancer\"]\n    N0 -->|\"expressed in\"| N2\n    N2 -->|\"expressed in\"| N0\n    N3[\"Neurodegeneration\"]\n    N0 -->|\"associated with\"| N3\n    N0 -->|\"associated with\"| N2\n    N4[\"Stroke\"]\n    N0 -->|\"associated with\"| N4\n    N0 -->|\"expressed in\"| N1\n    N5[\"METABOLIC HEALTH\"]\n    N0 -->|\"regulates\"| N5\n    N6[\"circadian rhythm\"]\n    N0 -->|\"drives\"| N6\n    N7[\"Circadian Rhythms\"]\n    N0 -->|\"involved in\"| N7\n    N8[\"Cellular Metabolism\"]\n    N0 -->|\"regulates\"| N8\n```\n\n## Why Artifacts?\n\nScientific claims need more than text — they need data, visualizations, and computational evidence. Artifacts make SciDEX discoveries:\n- **Transparent** — See the data behind the claim\n- **Reproducible** — Access the code and methodology\n- **Inspectable** — Drill down into details\n- **Citable** — Each artifact has a permanent ID\n\n## Artifact Types\n\nSciDEX tracks **37,664 artifacts** across 17 types:\n\n| Type | Count | Description |\n|------|-------|-------------|\n| `wiki_page` | 18,434 | Wiki page exports and summaries |\n| `figure` | 9,603 | Generated charts, plots, and diagrams |\n| `kg_edge` | 7,618 | Knowledge graph relationship evidence |\n| `experiment` | 631 | Computational experiments |\n| `paper` | 520 | Scientific papers and preprints |\n| `notebook` | 233 | Jupyter computational notebooks |\n| `paper_figure` | 216 | Extracted figures from literature |\n| `hypothesis` | 200 | Hypothesis snapshots with provenance |\n| `analysis` | 105 | Structured analysis reports |\n| `protein_design` | 21 | Protein structure predictions |\n| `model` | 7 | Computational models |\n| `dataset` | 6 | Curated datasets |\n| `dashboard` | 5 | Interactive data dashboards |\n| `tabular_dataset` | 3 | Structured tabular data |\n| `ai_image` | 4 | AI-generated scientific images |\n| `authored_paper` | 2 | Full paper summaries |\n| `code` | 1 | Source code artifacts |\n\n## Artifact Registry\n\nAll artifacts are tracked in the `artifacts` table with:\n- **Unique ID** — Permanent reference\n- **Type** — Classification (notebook, figure, etc.)\n- **Parent IDs** — What hypothesis/analysis/entity it supports\n- **Metadata** — Tags, quality score, visibility\n- **Storage** — File path or database reference\n\n## Protein Structure Visualization (Quest 17)\n\nOne of SciDEX's most powerful artifact types is **interactive 3D protein structure viewers** embedded directly in hypothesis and entity pages. Using the Mol* viewer (the same technology powering RCSB PDB and AlphaFold), users can explore protein structures mentioned in scientific claims.\n\n### How Protein Viewers Work\n\nSciDEX uses a three-tier fallback strategy to maximize structure coverage:\n\n#### 1. PDB Experimental Structures (Preferred)\n\nWhen available, SciDEX displays high-resolution experimental structures from the RCSB Protein Data Bank:\n- X-ray crystallography structures\n- Cryo-EM electron microscopy maps\n- NMR solution structures\n\n**Example genes with PDB structures:** TREM2, MAPT, SNCA, APP, PSEN1\n\n#### 2. AlphaFold Predicted Structures (Fallback)\n\nFor human proteins without experimental structures, SciDEX uses AlphaFold predictions via UniProt IDs:\n- AI-predicted structures with per-residue confidence scores\n- Covers most of the human proteome\n- Useful for intrinsically disordered regions and novel proteins\n\n#### 3. Dynamic PDB Search (Fallback)\n\nIf no pre-mapped structure exists, SciDEX queries the RCSB PDB API in real-time:\n- Live search by gene name\n- Returns best-matching structure\n- Graceful degradation to external links if no structure found\n\n### Viewer Features\n\nInteractive controls for all viewers:\n- **Rotate:** Click and drag\n- **Zoom:** Mouse scroll\n- **Reset:** Right-click\n- **Lazy-loading:** Viewers only render when expanded (performance optimization)\n- **External links:** Direct links to PDB/AlphaFold source pages\n\n### Where to Find Protein Viewers\n\n- **Hypothesis pages:** Automatically embedded when `target_gene` is specified\n- **Entity pages:** Protein entity pages show all available structures\n- **Drug target pages:** Therapeutic target pages highlight binding sites and domains\n\n**Current coverage:** 50+ proteins with interactive 3D viewers across demo hypotheses and high-priority entities.\n\n## Using Artifacts\n\n### On Hypothesis Pages\nArtifacts appear in dedicated sections:\n- \"Evidence Visualizations\" for figures\n- \"Computational Analysis\" for notebooks\n- \"Supporting Literature\" for paper figures\n\n### In the Artifact Gallery\nBrowse all artifacts at `/artifacts`:\n- Filter by type\n- Search by keyword\n- Sort by quality or recency\n\n### Via API\nProgrammatic access via `/api/artifacts`:\n```\nGET /api/artifacts?type=notebook&parent_type=hypothesis&parent_id=abc123\n```\n\n## Artifact Quality\n\nNot all artifacts are equal. Quality indicators:\n- **Confidence score** — System-assessed reliability\n- **Citation count** — How often referenced\n- **Review status** — Manual curation for high-visibility pages\n\nDemo pages feature only high-quality, reviewed artifacts.\n\n## Creating Artifacts\n\n- **Forge agents** generate most artifacts during analysis workflows\n- **Human contributors** can submit via `/join` registration\n- **Quality gates** ensure artifacts meet standards before publication\n\n**See also:** [Notebooks](/docs/notebooks), [Forge Layer](/docs/five-layers)",
  "entity_type": "scidex_docs"
}

{
  "content_md": "# Rich Artifacts\n\nArtifacts are generated research outputs that enrich hypotheses and analyses with inspectable evidence. They range from figures and notebooks to protein structures and knowledge graph edges.\n\n## Why Artifacts?\n\nScientific claims need more than text — they need data, visualizations, and computational evidence. Artifacts make SciDEX discoveries:\n- **Transparent** — See the data behind the claim\n- **Reproducible** — Access the code and methodology\n- **Inspectable** — Drill down into details\n- **Citable** — Each artifact has a permanent ID\n\n## Artifact Types\n\nSciDEX tracks **37,664 artifacts** across 17 types:\n\n| Type | Count | Description |\n|------|-------|-------------|\n| `wiki_page` | 18,434 | Wiki page exports and summaries |\n| `figure` | 9,603 | Generated charts, plots, and diagrams |\n| `kg_edge` | 7,618 | Knowledge graph relationship evidence |\n| `experiment` | 631 | Computational experiments |\n| `paper` | 520 | Scientific papers and preprints |\n| `notebook` | 233 | Jupyter computational notebooks |\n| `paper_figure` | 216 | Extracted figures from literature |\n| `hypothesis` | 200 | Hypothesis snapshots with provenance |\n| `analysis` | 105 | Structured analysis reports |\n| `protein_design` | 21 | Protein structure predictions |\n| `model` | 7 | Computational models |\n| `dataset` | 6 | Curated datasets |\n| `dashboard` | 5 | Interactive data dashboards |\n| `tabular_dataset` | 3 | Structured tabular data |\n| `ai_image` | 4 | AI-generated scientific images |\n| `authored_paper` | 2 | Full paper summaries |\n| `code` | 1 | Source code artifacts |\n\n## Artifact Registry\n\nAll artifacts are tracked in the `artifacts` table with:\n- **Unique ID** — Permanent reference\n- **Type** — Classification (notebook, figure, etc.)\n- **Parent IDs** — What hypothesis/analysis/entity it supports\n- **Metadata** — Tags, quality score, visibility\n- **Storage** — File path or database reference\n\n## Protein Structure Visualization (Quest 17)\n\nOne of SciDEX's most powerful artifact types is **interactive 3D protein structure viewers** embedded directly in hypothesis and entity pages. Using the Mol* viewer (the same technology powering RCSB PDB and AlphaFold), users can explore protein structures mentioned in scientific claims.\n\n### How Protein Viewers Work\n\nSciDEX uses a three-tier fallback strategy to maximize structure coverage:\n\n#### 1. PDB Experimental Structures (Preferred)\n\nWhen available, SciDEX displays high-resolution experimental structures from the RCSB Protein Data Bank:\n- X-ray crystallography structures\n- Cryo-EM electron microscopy maps\n- NMR solution structures\n\n**Example genes with PDB structures:** TREM2, MAPT, SNCA, APP, PSEN1\n\n#### 2. AlphaFold Predicted Structures (Fallback)\n\nFor human proteins without experimental structures, SciDEX uses AlphaFold predictions via UniProt IDs:\n- AI-predicted structures with per-residue confidence scores\n- Covers most of the human proteome\n- Useful for intrinsically disordered regions and novel proteins\n\n#### 3. Dynamic PDB Search (Fallback)\n\nIf no pre-mapped structure exists, SciDEX queries the RCSB PDB API in real-time:\n- Live search by gene name\n- Returns best-matching structure\n- Graceful degradation to external links if no structure found\n\n### Viewer Features\n\nInteractive controls for all viewers:\n- **Rotate:** Click and drag\n- **Zoom:** Mouse scroll\n- **Reset:** Right-click\n- **Lazy-loading:** Viewers only render when expanded (performance optimization)\n- **External links:** Direct links to PDB/AlphaFold source pages\n\n### Where to Find Protein Viewers\n\n- **Hypothesis pages:** Automatically embedded when `target_gene` is specified\n- **Entity pages:** Protein entity pages show all available structures\n- **Drug target pages:** Therapeutic target pages highlight binding sites and domains\n\n**Current coverage:** 50+ proteins with interactive 3D viewers across demo hypotheses and high-priority entities.\n\n## Using Artifacts\n\n### On Hypothesis Pages\nArtifacts appear in dedicated sections:\n- \"Evidence Visualizations\" for figures\n- \"Computational Analysis\" for notebooks\n- \"Supporting Literature\" for paper figures\n\n### In the Artifact Gallery\nBrowse all artifacts at `/artifacts`:\n- Filter by type\n- Search by keyword\n- Sort by quality or recency\n\n### Via API\nProgrammatic access via `/api/artifacts`:\n```\nGET /api/artifacts?type=notebook&parent_type=hypothesis&parent_id=abc123\n```\n\n## Artifact Quality\n\nNot all artifacts are equal. Quality indicators:\n- **Confidence score** — System-assessed reliability\n- **Citation count** — How often referenced\n- **Review status** — Manual curation for high-visibility pages\n\nDemo pages feature only high-quality, reviewed artifacts.\n\n## Creating Artifacts\n\n- **Forge agents** generate most artifacts during analysis workflows\n- **Human contributors** can submit via `/join` registration\n- **Quality gates** ensure artifacts meet standards before publication\n\n**See also:** [Notebooks](/docs/notebooks), [Forge Layer](/docs/five-layers)",
  "entity_type": "scidex_docs"
}

{
  "content_md": "# Rich Artifacts\n\nArtifacts are generated research outputs that enrich hypotheses and analyses with inspectable evidence. They range from figures and notebooks to protein structures and knowledge graph edges.\n\n## Why Artifacts?\n\nScientific claims need more than text — they need data, visualizations, and computational evidence. Artifacts make SciDEX discoveries:\n- **Transparent** — See the data behind the claim\n- **Reproducible** — Access the code and methodology\n- **Inspectable** — Drill down into details\n- **Citable** — Each artifact has a permanent ID\n\n## Artifact Types\n\nSciDEX tracks **2,935 artifacts** across 13 types:\n\n| Type | Count | Description |\n|------|-------|-------------|\n| `wiki` | 1,776 | Wiki page exports and summaries |\n| `paper_figure` | 264 | Extracted figures from literature |\n| `figure` | 232 | Generated charts, plots, and diagrams |\n| `notebook` | 211 | Jupyter computational notebooks |\n| `hypothesis` | 181 | Hypothesis snapshots with provenance |\n| `kg_edge` | 180 | Knowledge graph relationship evidence |\n| `analysis` | 80 | Structured analysis reports |\n| `protein_design` | 6 | Protein structure predictions |\n| `authored_paper` | 1 | Full paper summaries |\n| `dashboard` | 1 | Interactive data dashboards |\n| `dataset` | 1 | Curated datasets |\n| `model` | 1 | Computational models |\n| `tabular_dataset` | 1 | Structured tabular data |\n\n## Artifact Registry\n\nAll artifacts are tracked in the `artifacts` table with:\n- **Unique ID** — Permanent reference\n- **Type** — Classification (notebook, figure, etc.)\n- **Parent IDs** — What hypothesis/analysis/entity it supports\n- **Metadata** — Tags, quality score, visibility\n- **Storage** — File path or database reference\n\n## Protein Structure Visualization (Quest 17)\n\nOne of SciDEX's most powerful artifact types is **interactive 3D protein structure viewers** embedded directly in hypothesis and entity pages. Using the Mol* viewer (the same technology powering RCSB PDB and AlphaFold), users can explore protein structures mentioned in scientific claims.\n\n### How Protein Viewers Work\n\nSciDEX uses a three-tier fallback strategy to maximize structure coverage:\n\n#### 1. PDB Experimental Structures (Preferred)\n\nWhen available, SciDEX displays high-resolution experimental structures from the RCSB Protein Data Bank:\n- X-ray crystallography structures\n- Cryo-EM electron microscopy maps\n- NMR solution structures\n\n**Example genes with PDB structures:** TREM2, MAPT, SNCA, APP, PSEN1\n\n#### 2. AlphaFold Predicted Structures (Fallback)\n\nFor human proteins without experimental structures, SciDEX uses AlphaFold predictions via UniProt IDs:\n- AI-predicted structures with per-residue confidence scores\n- Covers most of the human proteome\n- Useful for intrinsically disordered regions and novel proteins\n\n#### 3. Dynamic PDB Search (Fallback)\n\nIf no pre-mapped structure exists, SciDEX queries the RCSB PDB API in real-time:\n- Live search by gene name\n- Returns best-matching structure\n- Graceful degradation to external links if no structure found\n\n### Viewer Features\n\nInteractive controls for all viewers:\n- **Rotate:** Click and drag\n- **Zoom:** Mouse scroll\n- **Reset:** Right-click\n- **Lazy-loading:** Viewers only render when expanded (performance optimization)\n- **External links:** Direct links to PDB/AlphaFold source pages\n\n### Where to Find Protein Viewers\n\n- **Hypothesis pages:** Automatically embedded when `target_gene` is specified\n- **Entity pages:** Protein entity pages show all available structures\n- **Drug target pages:** Therapeutic target pages highlight binding sites and domains\n\n**Current coverage:** 50+ proteins with interactive 3D viewers across demo hypotheses and high-priority entities.\n\n## Using Artifacts\n\n### On Hypothesis Pages\nArtifacts appear in dedicated sections:\n- \"Evidence Visualizations\" for figures\n- \"Computational Analysis\" for notebooks\n- \"Supporting Literature\" for paper figures\n\n### In the Artifact Gallery\nBrowse all artifacts at `/artifacts`:\n- Filter by type\n- Search by keyword\n- Sort by quality or recency\n\n### Via API\nProgrammatic access via `/api/artifacts`:\n```\nGET /api/artifacts?type=notebook&parent_type=hypothesis&parent_id=abc123\n```\n\n## Artifact Quality\n\nNot all artifacts are equal. Quality indicators:\n- **Confidence score** — System-assessed reliability\n- **Citation count** — How often referenced\n- **Review status** — Manual curation for high-visibility pages\n\nDemo pages feature only high-quality, reviewed artifacts.\n\n## Creating Artifacts\n\n- **Forge agents** generate most artifacts during analysis workflows\n- **Human contributors** can submit via `/join` registration\n- **Quality gates** ensure artifacts meet standards before publication\n\n**See also:** [Notebooks](/docs/notebooks), [Forge Layer](/docs/five-layers)",
  "entity_type": "scidex_docs"
}