[
  {
    "title": "SPHERE-I-SCIENCE",
    "sphere": "science",
    "track": "Root sphere repo",
    "entryType": "repository",
    "status": "active",
    "summary": "Computational science research across oncology, plant science, metabolomics, neuroscience, ecology, and life systems.",
    "tags": [
      "science",
      "root sphere repo",
      "s",
      "repository",
      "sphere-i-science",
      "computational",
      "oncology"
    ],
    "links": [
      {
        "label": "Repo",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE"
      },
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/README.md"
      }
    ],
    "primarySphere": "science",
    "secondarySpheres": [],
    "combo": "S",
    "artifactType": "repository",
    "deliveryLayers": [
      "GitHub"
    ],
    "validationStage": "taxonomy"
  },
  {
    "title": "S1 Biomedical And Oncology",
    "sphere": "science",
    "track": "S1",
    "entryType": "lane",
    "status": "active",
    "summary": "S1 is the biomedical and translational oncology block inside SPHERE-I-SCIENCE.",
    "tags": [
      "science",
      "s1",
      "s",
      "lane",
      "biomedical",
      "oncology",
      "translational"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/README.md"
      },
      {
        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology"
      }
    ],
    "primarySphere": "science",
    "secondarySpheres": [],
    "combo": "S",
    "artifactType": "lane",
    "deliveryLayers": [
      "GitHub"
    ],
    "validationStage": "taxonomy"
  },
  {
    "title": "OpenVariant: An Open-Source Variant Pathogenicity Classifier Benchmarked Against AlphaMissense",
    "sphere": "science",
    "track": "S1-A-R1",
    "entryType": "study",
    "status": "active",
    "summary": "Model performance: AUC-ROC = 0.942 (XGBoost, target 0.939) | AUC-ROC = 0.935 (AlphaMissense placeholder, target 0.934) | Dataset: N = 1,804 (⚠ SIMULATED)",
    "tags": [
      "science",
      "s1-a-r1",
      "s+t",
      "research-tool",
      "openvariant",
      "open-source",
      "variant"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-A%20%C2%B7%20%F0%9F%A7%AC%20PHYLO-GENOMICS/S1-A-R1%20%C2%B7%20Variant%20classification/R1a-openvariant/README.md"
      },
      {
        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-A%20%C2%B7%20%F0%9F%A7%AC%20PHYLO-GENOMICS/S1-A-R1%20%C2%B7%20Variant%20classification/R1a-openvariant"
      }
    ],
    "primarySphere": "science",
    "secondarySpheres": [
      "technology"
    ],
    "combo": "S+T",
    "artifactType": "research_tool",
    "deliveryLayers": [
      "GitHub"
    ],
    "validationStage": "prototype"
  },
  {
    "title": "Identification of Tumor Suppressor miRNAs Silenced in BRCA2-Mutant Breast Cancer: A Multi-Dataset Meta-Analysis",
    "sphere": "science",
    "track": "S1-B-R1",
    "entryType": "study",
    "status": "active",
    "summary": "Model performance: 25 significant DE miRNAs identified (padj ≤ 0.05, |log2FC| ≥ 0.3) | Dataset: N = 300 (13 BRCA2-mutant, 287 wildtype) — ⚠️ SIMULATED DATA",
    "tags": [
      "science",
      "s1-b-r1",
      "s",
      "hypothesis",
      "identification",
      "tumor",
      "suppressor"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-B%20%C2%B7%20%F0%9F%94%AC%20PHYLO-RNA/S1-B-R1%20%C2%B7%20miRNA%20silencing/R1a-brca2-mirna/README.md"
      },
      {
        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-B%20%C2%B7%20%F0%9F%94%AC%20PHYLO-RNA/S1-B-R1%20%C2%B7%20miRNA%20silencing/R1a-brca2-mirna"
      }
    ],
    "primarySphere": "science",
    "secondarySpheres": [],
    "combo": "S",
    "artifactType": "hypothesis",
    "deliveryLayers": [
      "GitHub"
    ],
    "validationStage": "exploratory"
  },
  {
    "title": "Computational Identification of siRNA Synthetic Lethal Targets in TP53-Mutant Lung Adenocarcinoma",
    "sphere": "science",
    "track": "S1-B-R2",
    "entryType": "study",
    "status": "active",
    "summary": "Pipeline validation: PLK1 and CDK1 recovered as positive clinical controls | Dataset: N = 566 (295 TP53-mut + 271 WT) [SIMULATED]",
    "tags": [
      "science",
      "s1-b-r2",
      "s+e+t",
      "research-tool",
      "computational",
      "identification",
      "sirna"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-B%20%C2%B7%20%F0%9F%94%AC%20PHYLO-RNA/S1-B-R2%20%C2%B7%20siRNA%20SL/R2a-tp53-sirna/README.md"
      },
      {
        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-B%20%C2%B7%20%F0%9F%94%AC%20PHYLO-RNA/S1-B-R2%20%C2%B7%20siRNA%20SL/R2a-tp53-sirna"
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    ],
    "primarySphere": "science",
    "secondarySpheres": [
      "entrepreneurship",
      "technology"
    ],
    "combo": "S+E+T",
    "artifactType": "research_tool",
    "deliveryLayers": [
      "GitHub"
    ],
    "validationStage": "prototype"
  },
  {
    "title": "lncRNA Regulatory Networks Controlling TREM2-Dependent Microglial Inflammation: Implications for Alzheimer's Therapy",
    "sphere": "science",
    "track": "S1-B-R3",
    "entryType": "study",
    "status": "active",
    "summary": "Data type: Simulated iPSC-derived microglia RNA-seq (TREM2-KO vs WT) | Dataset: 2 independent simulated datasets × 12 samples each (not biological replicates of one another)",
    "tags": [
      "science",
      "s1-b-r3",
      "s",
      "hypothesis",
      "lncrna",
      "regulatory",
      "networks"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-B%20%C2%B7%20%F0%9F%94%AC%20PHYLO-RNA/S1-B-R3%20%C2%B7%20lncRNA%20%2B%20ASO/R3a-lncrna-trem2/README.md"
      },
      {
        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-B%20%C2%B7%20%F0%9F%94%AC%20PHYLO-RNA/S1-B-R3%20%C2%B7%20lncRNA%20%2B%20ASO/R3a-lncrna-trem2"
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    ],
    "primarySphere": "science",
    "secondarySpheres": [],
    "combo": "S",
    "artifactType": "hypothesis",
    "deliveryLayers": [
      "GitHub"
    ],
    "validationStage": "exploratory"
  },
  {
    "title": "Computational Discovery of Small Molecules Targeting FGFR3 mRNA for Bladder Cancer",
    "sphere": "science",
    "track": "S1-C-R1",
    "entryType": "study",
    "status": "active",
    "summary": "Model performance: Top-2 RNA-binding score = 0.793 / 0.789 (SIMULATED) | Dataset: N = 200 compounds (SIMULATED virtual screen)",
    "tags": [
      "science",
      "s1-c-r1",
      "s+t",
      "research-tool",
      "computational",
      "discovery",
      "small"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-C%20%C2%B7%20%F0%9F%92%8A%20PHYLO-DRUG/S1-C-R1%20%C2%B7%20RNA-directed%20drug/R1a-fgfr3-rna-drug/README.md"
      },
      {
        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-C%20%C2%B7%20%F0%9F%92%8A%20PHYLO-DRUG/S1-C-R1%20%C2%B7%20RNA-directed%20drug/R1a-fgfr3-rna-drug"
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    ],
    "primarySphere": "science",
    "secondarySpheres": [
      "technology"
    ],
    "combo": "S+T",
    "artifactType": "research_tool",
    "deliveryLayers": [
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    ],
    "validationStage": "prototype"
  },
  {
    "title": "Machine Learning Prediction of Protein Corona Composition in Lipid Nanoparticles from Physicochemical Properties",
    "sphere": "science",
    "track": "S1-D-R1",
    "entryType": "study",
    "status": "active",
    "summary": "Model performance: Macro-OvR AUC = 0.791 (reported) / 0.836 [SIMULATED-CIRCULAR] | Dataset: N = 19,200 (LNPDB, simulated for this demo)",
    "tags": [
      "science",
      "s1-d-r1",
      "s+t",
      "research-tool",
      "machine",
      "learning",
      "prediction"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R1%20%C2%B7%20Serum%20corona/R1a-lnp-corona-ml/README.md"
      },
      {
        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R1%20%C2%B7%20Serum%20corona/R1a-lnp-corona-ml"
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    ],
    "primarySphere": "science",
    "secondarySpheres": [
      "technology"
    ],
    "combo": "S+T",
    "artifactType": "research_tool",
    "deliveryLayers": [
      "GitHub"
    ],
    "validationStage": "prototype"
  },
  {
    "title": "Machine Learning Prediction of Protein Corona Composition in Lipid Nanoparticles from Physicochemical Properties",
    "sphere": "science",
    "track": "S1-D-R2",
    "entryType": "study",
    "status": "active",
    "summary": "Model performance: XGBoost AUC = 0.877 (5-fold CV, simulated; target spec: 0.791) | Corona PoC AUC = 0.834 (LOOCV) | Dataset: N = 19,200 (SIMULATED)",
    "tags": [
      "science",
      "s1-d-r2",
      "s",
      "hypothesis",
      "machine",
      "learning",
      "prediction"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R2%20%C2%B7%20Flow%20corona/R2a-flow-corona/study1/README.md"
      },
      {
        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R2%20%C2%B7%20Flow%20corona/R2a-flow-corona/study1"
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    "primarySphere": "science",
    "secondarySpheres": [],
    "combo": "S",
    "artifactType": "hypothesis",
    "deliveryLayers": [
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    ],
    "validationStage": "exploratory"
  },
  {
    "title": "Predicting Protein Corona Remodeling in Lipid Nanoparticles Under Physiological Flow: Closing the Static-Dynamic Gap",
    "sphere": "science",
    "track": "S1-D-R2",
    "entryType": "study",
    "status": "active",
    "summary": "Model performance: RF Train R² = 0.781 | LOOCV R² = −0.281 (underpowered, N=32) | Dataset: N = 32 matched pairs (SIMULATED)",
    "tags": [
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      "s1-d-r2",
      "s+t",
      "research-tool",
      "predicting",
      "protein",
      "corona"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R2%20%C2%B7%20Flow%20corona/R2a-flow-corona/study2/README.md"
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        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R2%20%C2%B7%20Flow%20corona/R2a-flow-corona/study2"
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    ],
    "primarySphere": "science",
    "secondarySpheres": [
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    ],
    "combo": "S+T",
    "artifactType": "research_tool",
    "deliveryLayers": [
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    ],
    "validationStage": "prototype"
  },
  {
    "title": "Ionizable Lipid Properties Predicting ApoE Enrichment in LNP Protein Corona for Blood-Brain Barrier Crossing in Glioblastoma",
    "sphere": "science",
    "track": "S1-D-R3",
    "entryType": "study",
    "status": "active",
    "summary": "Model performance: LOO-CV R² = 0.542 (overall; dominated by lipid-type confound — within-group ionizable R²=−1.571), Pearson r = 0.780, MAE = 4.9% | Dataset: N = 22 (SIMULATED — literature-grounded ranges)",
    "tags": [
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      "s1-d-r3",
      "s+t",
      "research-tool",
      "ionizable",
      "lipid",
      "properties"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R3%20%C2%B7%20Brain%20BBB/R3a-lnp-bbb/README.md"
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        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R3%20%C2%B7%20Brain%20BBB/R3a-lnp-bbb"
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    ],
    "primarySphere": "science",
    "secondarySpheres": [
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    ],
    "combo": "S+T",
    "artifactType": "research_tool",
    "deliveryLayers": [
      "GitHub"
    ],
    "validationStage": "prototype"
  },
  {
    "title": "AutoCorona: An NLP Pipeline for Automated Extraction of LNP Protein Corona Data from Scientific Literature",
    "sphere": "science",
    "track": "S1-D-R4",
    "entryType": "study",
    "status": "active",
    "summary": "Model performance: F1 = 0.71 (proteinsource) | Dataset: N = 43 entries (22 GS + 21 new)",
    "tags": [
      "science",
      "s1-d-r4",
      "s+t",
      "research-tool",
      "autocorona",
      "nlp",
      "pipeline"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R4%20%C2%B7%20NLP/R4a-autocorona-nlp/project2_autocorona/README.md"
      },
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        "label": "Folder",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R4%20%C2%B7%20NLP/R4a-autocorona-nlp/project2_autocorona"
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    ],
    "primarySphere": "science",
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    ],
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    "deliveryLayers": [
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    ],
    "validationStage": "prototype"
  },
  {
    "title": "K R&D Lab — LNP Corona Research Projects",
    "sphere": "science",
    "track": "S1-D-R4",
    "entryType": "study",
    "status": "active",
    "summary": "![Demo](https://huggingface.co/spaces/K-RnD-Lab/Learning-Playground03-2026)",
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      "research-tool",
      "lab",
      "lnp",
      "corona"
    ],
    "links": [
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        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/tree/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R4%20%C2%B7%20NLP/R4a-autocorona-nlp"
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    ],
    "primarySphere": "science",
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    "combo": "S+T",
    "artifactType": "research_tool",
    "deliveryLayers": [
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    ],
    "validationStage": "prototype"
  },
  {
    "title": "Machine Learning Prediction of Protein Corona Composition in Lipid Nanoparticles from Physicochemical Properties",
    "sphere": "science",
    "track": "S1-D-R4",
    "entryType": "study",
    "status": "scaffold",
    "summary": "Model performance: XGBoost AUC = 0.791 (5-fold CV) | Dataset: N = 19,200 transfection records",
    "tags": [
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      "s1-d-r4",
      "s",
      "hypothesis",
      "machine",
      "learning",
      "prediction"
    ],
    "links": [
      {
        "label": "README",
        "href": "https://github.com/K-RnD-Lab/SPHERE-I-SCIENCE/blob/main/S1%20%E2%80%94%20%F0%9F%A9%BA%20%20Biomedical%20%26%20Oncology/S1-D%20%C2%B7%20%F0%9F%A7%AA%20PHYLO-LNP/S1-D-R4%20%C2%B7%20NLP/R4a-autocorona-nlp/project1_lnp_ml/README.md"
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    "title": "S7-I · 🔎 Career or Education",
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    "links": [
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  {
    "title": "S7-J · 🌿 Environmental or Charity",
    "sphere": "science",
    "track": "S7-J",
    "entryType": "study",
    "status": "active",
    "summary": "A sub-lane inside 📚 S7 — K Life OS.",
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    "links": [
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    "title": "S7-K · 👥 Personal Relationship",
    "sphere": "science",
    "track": "S7-K",
    "entryType": "study",
    "status": "active",
    "summary": "A sub-lane inside 📚 S7 — K Life OS.",
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    "title": "S7-L · 🧘 Spirituality",
    "sphere": "science",
    "track": "S7-L",
    "entryType": "study",
    "status": "active",
    "summary": "A sub-lane inside 📚 S7 — K Life OS.",
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    "links": [
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    "title": "S7-M · 🧭 Longitudinal Reviews & Life Wheel Synthesis",
    "sphere": "science",
    "track": "S7-M",
    "entryType": "study",
    "status": "active",
    "summary": "A meta-lane inside 📚 S7 - K Life OS.",
    "tags": [
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      "s",
      "hypothesis",
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      "reviews",
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    "links": [
      {
        "label": "README",
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    "title": "SPHERE-II-ENTREPRENEURSHIP",
    "sphere": "entrepreneurship",
    "track": "Root sphere repo",
    "entryType": "repository",
    "status": "active",
    "summary": "Applied research for venture design, market intelligence, ecosystem mapping, and public cases.",
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    "artifactType": "repository",
    "deliveryLayers": [
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    "validationStage": "taxonomy"
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    "title": "R1a Lab-to-Market Opportunity Map",
    "sphere": "entrepreneurship",
    "track": "E1-R1",
    "entryType": "study",
    "status": "scaffold",
    "summary": "Which translational opportunity spaces are the most plausible first commercial or collaboration paths for outputs generated inside K R&D Lab?",
    "tags": [
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      "venture-case",
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      "opportunity"
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    "links": [
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    "title": "R1a Translational Audience Segments",
    "sphere": "entrepreneurship",
    "track": "E2-R1",
    "entryType": "study",
    "status": "scaffold",
    "summary": "Which audience groups are most distinct and strategically relevant for K R&D Lab outputs across science, tooling, and public-facing research artifacts?",
    "tags": [
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    "validationStage": "scaffold"
  },
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    "title": "R1a Bio-AI Translation Landscape",
    "sphere": "entrepreneurship",
    "track": "E3-R1",
    "entryType": "public case",
    "status": "active",
    "summary": "Which ecosystems, conferences, partners, and open communities matter most for translating K R&D Lab work across science, tooling, and applied public cases?",
    "tags": [
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    "links": [
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    "title": "R1a Three-Sphere Research Ops Case",
    "sphere": "entrepreneurship",
    "track": "E4-A",
    "entryType": "public case",
    "status": "active",
    "summary": "How should K R&D Lab structure research, tooling, and public-facing artifacts across GitHub and Hugging Face so the whole ecosystem stays navigable, testable, and reusable?",
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      "e4-a",
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      "public-case",
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    "links": [
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    "title": "SPHERE-III-TECHNOLOGY",
    "sphere": "technology",
    "track": "Root sphere repo",
    "entryType": "repository",
    "status": "active",
    "summary": "Reusable research tools, scoring systems, dashboards, and open infrastructure for K R&D Lab.",
    "tags": [
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    "links": [
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    "artifactType": "repository",
    "deliveryLayers": [
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    "validationStage": "taxonomy"
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    "title": "R1a Bioinformatics Pipeline Template",
    "sphere": "technology",
    "track": "T1-R1",
    "entryType": "study",
    "status": "scaffold",
    "summary": "How should K R&D Lab package reusable analytical engines so the same method can support multiple science studies without rewriting the workflow each time?",
    "tags": [
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    "links": [
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    "artifactType": "tool",
    "deliveryLayers": [
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    "validationStage": "prototype"
  },
  {
    "title": "R1a Study Readiness Scoring",
    "sphere": "technology",
    "track": "T2-R1",
    "entryType": "study",
    "status": "scaffold",
    "summary": "How can K R&D Lab score whether a study is ready to move from exploratory computational work into a more reproducible or experimentally actionable stage?",
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      "s+t",
      "scoring-system",
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    "links": [
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    "combo": "S+T",
    "artifactType": "scoring_system",
    "deliveryLayers": [
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    ],
    "validationStage": "prototype"
  },
  {
    "title": "R1a Study Registry Dashboard Template",
    "sphere": "technology",
    "track": "T3-R1",
    "entryType": "study",
    "status": "scaffold",
    "summary": "What is the minimal reusable dashboard or registry pattern that can make K R&D Lab studies easier to browse, compare, and audit across spheres?",
    "tags": [
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      "minimal"
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    "links": [
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    ],
    "primarySphere": "technology",
    "secondarySpheres": [
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    "combo": "S+E+T",
    "artifactType": "dashboard",
    "deliveryLayers": [
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    ],
    "validationStage": "live prototype"
  }
]