docs/DATA_DICTIONARY.md

Data dictionary

Core schemas and principal annex objects, plus the contracts that make the layers join. Detailed module schemas are linked beside their data. Contexts (8): zel024_hek293, zel024_h1650, zel028_hek293, zel028_a549, zel028_h1650, zel031_a549, zel031_thp1, zel039_aec7. Compound counts per context: 13,914 / 10,686 / 61,396 / 40,622 / 25,906 / 8,321 / 9,041 / 20,813 (190,699 compound-context pairs; 162,914 distinct compounds).

Contracts (read first)

Row-alignment contract. Every {context}.npy matrix (in core/surfaces/ and core/usages/) is row-aligned to its sibling {context}_compounds.parquet: row i of the matrix is the compound named in row i of the parquet, and the parquet has exactly one column, public_compound_id. Surface, usage, and compounds tables for the same context have the same row count and the same row order. There is no other join key and no index column; positional alignment is the contract.

Panel index-space contract. The harmonized panel is defined by core/surfaces/harmonized_6000_genes.parquet. Its gene_index column holds raw positional indices into the 46,944-gene pseudobulk space. Gene symbols for panel positions come ONLY from this file's gene column; never map panel positions through any context's released 3,000-gene panel, which is a different index space.

Usages/basis contract. Usage column j (0-based) is program Pj+1 of the pinned basis in core/basis/ for the core usages and aligned prediction dumps. Usages are valid only against that basis (core/basis/basis_registry.json); a refit changes program identities. The annotated program atlas uses a separate label namespace; follow its documented mapping and scope. Same-well D00–D31 coordinates are a separate representation.

Fold convention. fold = SHA256(public_compound_id) mod 5, computed on the full 64-hex-character digest as an integer. The shared basis and the reference model were both fit with fold 0 held out. Train on folds 1-4, evaluate on fold 0. Note: promoted usages can be slightly more predictable on fold 0 in some exercises, so clean cross-exercise comparisons use folds 1-4.

Measured vs predicted. Core surfaces are processed measurements; usages are their projection onto a learned basis. Recipes and splits are metadata; the basis is a fitted representation, and benchmark tables contain model-evaluation results. models/ outputs, the y_pred_z arrays in the imaging annex dumps, and any table marked grammar_level or model-predicted are predictions, and are labeled as such where they ship.

Empirical-p floors. Empirical p-values are floored at the resolution of their null: 1/1001 in the phenomimicry validation tables (1,000-draw nulls) and 1/201 wherever a 200-draw null is used. Permutation p-values in the chemistry tables ship as separate *_p_permutation columns alongside the empirical ones. A printed p=0.000 in the hypothesis ledger means the value fell below the 200-draw floor, not that it is exactly zero.

core/

core/usages/

• usages_{context}.npy: float32, (n_compounds, 32), free sign. Per-compound program usages (program_usage_projection_v1): the free-sign least-squares projection of the context-scaled harmonized surface onto the pinned k = 32 basis.
• usages_{context}_compounds.parquet: one column: public_compound_id (string). One row per usage-matrix row, aligned positionally.

core/surfaces/

• surfaces_{context}.npy: float32, (n_compounds, 6000). Device-centered log1p-CP10k expression on the harmonized panel (device_centered_harmonized_surface_v1). Column j is panel position j of harmonized_6000_genes.parquet.
• {context}_compounds.parquet: one column: public_compound_id.
• harmonized_6000_genes.parquet: 6,000 rows, the panel definition (harmonized_panel_6000_v1):
  • panel_position (int64, 0-5999): column index in every surfaces_{context}.npy.
  • gene_index (int64): raw position in the 46,944-gene pseudobulk space (full-width index space; see the contract above).
  • gene (string): gene symbol. The only authoritative symbol mapping.

core/basis/

• shared_basis_k32.npy: float32, (32, 6000), nonnegative (shared_program_basis_v1). Row j is the gene-loading vector of program Pj+1, columns aligned to the panel.
• shared_basis_k12.npy: float32, (12, 6000), nonnegative (shared_program_basis_k12_v1), lower-resolution companion.
• basis_registry.json: the version pin:
  • registry_name, basis_version (shared_program_basis_v1).
  • basis_fit_scope: training folds 1-4 only; fold 0 untouched by any fitting.
  • pinned_files: list of {package_name, file, k, sha256} for the two basis arrays.
  • validity_statement: usages are valid only against the pinned basis files.

core/recipes.parquet

162,914 rows, one per distinct public_compound_id:

• public_compound_id (string, CPD_############).
• bb0-bb4 (string, BB_##########, or null when the position is absent in that recipe). Position sets differ between libraries.
• n_positions_occupied (int64): how many of the five positions carry a block.

core/splits/fold_assignments.parquet

190,699 rows (= total compound count across the 8 contexts):

• context (string), public_compound_id (string), fold (int64, 0-4).

core/benchmark/

README.md explains the two configurations (per_context_expert, context_token_trunk), the two splits (compound_5fold, bb_disjoint), and the two output spaces (program space vs decoded gene space). Tables:

• per_context_comparison_k32.csv (16 rows): context, split_type, per_context_expert_mean, per_context_expert_sd_cells, per_context_expert_n_compounds, context_token_trunk_mean, context_token_trunk_sd_cells, context_token_trunk_n_compounds, diff_mean, diff_ci_lo, diff_ci_hi. Decoded gene-space mean per-compound Pearson (mcPearson) per model with the paired difference and its 95% confidence interval.
• program_space_primary.csv (90 rows): arm, split_type, target_variant, k, context, eval_only, n_runs, prog_pearson_mean, prog_pearson_sd_programs, null_mean, null_sd, z. Mean per-program Pearson of predicted vs measured usage coordinates against a permutation null, with the resulting z-score. Includes the probe context zel031_h1650 (never trained).
• k_resolution.csv (64 rows): context, split_type, arm, k, decoded_mcpearson_mean, decoded_mcpearson_sd_cells, prog_pearson_mean, prog_pearson_sd. k = 12 vs k = 32.
• correction_arm.csv (12 rows): context, arm, target_variant (device_centered_target / depth_regressed_target), k, n_cells, prog_pearson_mean, prog_pearson_sd, decoded_vs_device_centered_target_mean, decoded_vs_device_centered_target_sd_cells, decoded_vs_depth_regressed_target_mean, decoded_vs_depth_regressed_target_sd_cells. The depth-regressed variant did not improve; the device-centered target is canonical.
• program_signal_concentration.csv (4 rows): analysis_layer, metric, range_min, range_max, note. Gene-space versus program-space repeat-measurement ceilings and cross-cell-line agreement; the evidence table for the "10-20× stronger in program space" claim.
• cross_context_probe.csv (9 rows): ctx_a, ctx_b, n_compounds, n_cells, measured_transfer_mean, per_context_expert_transfer_mean, per_context_expert_transfer_sd_cells, per_context_expert_measured_pred_corr, per_context_expert_measured_pred_corr_sd, context_token_trunk_transfer_mean, context_token_trunk_transfer_sd_cells, context_token_trunk_measured_pred_corr, context_token_trunk_measured_pred_corr_sd, target_variant, k, prog_pearson_mean, prog_pearson_sd, null_mean, null_sd, z. ctx_b = probe_only_never_trained rows are the never-trained cell-line probe.
• fold0_baseline_comparison.csv (72 rows): context, model, split, n_train, n_test, tuning_parameter, tuning_value, mean_program_pearson, median_program_pearson, min_program_pearson, max_program_pearson, n_programs_with_defined_pearson, mean_compound_pearson, global_r2_vs_train_mean, rmse, source_or_fit. Per-context fold-0 test scores for a context-only train-mean control, three transparent recipe/building-block models, and the three reference-transformer seeds with their ensemble.
• fold0_reference_model_summary.csv (8 rows): one row per context with the control score, the simple-model scores, the three-seed transformer ensemble score, and the winner.

models/

See models/README.md for usage and the fold-0 convention.

• context_token_trunk_reference_eval_v1_seed{0,1,2}.pt: PyTorch checkpoints (torch.load(..., weights_only=True)), each a plain dict:
  • format: context_token_trunk_reference_eval_v1.
  • state_dict: model weights (keys match model_def.py exactly).
  • architecture: d_model 128, n_heads 4, n_layers 2, feedforward 512, dropout 0.1, n_programs 32, n_tokens 8, d_chemistry 128, d_identity 64, n_libraries, n_cell_lines, vocabulary_sizes (per bb slot), n_parameters 473,120.
  • training: held_out_fold 0, seed, split_convention compound_5fold, target device_centered_program_usage, basis_version shared_program_basis_v1, max_epochs, batch_size, learning_rate, weight_decay, warmup_epochs, patience, best_epoch, epochs_run, best_val_score, training_contexts.
  • vocabulary: bb_slots (bb0-bb4); level_to_index (per-slot maps public BB ID → identity-embedding row; row 0 = absent slot, row 1 = unseen ID); libraries; cell_lines; context_tokens (per context {library, cell_line, trained}; the probe context is marked trained: false).
  • usage_scales: per context {mu, sd} (32 values each): the per-context standardization mapping the raw head output (usage_z_scored) back to usage units comparable to core/usages/ (usage = usage_z_scored * sd + mu).
  • provenance: role ("reference model, evaluation grade").
• bb_embedding_table.parquet: 629 rows: public_bb_id (string) + embedding_000…embedding_127 (float32), the 128-dimensional pretrained chemistry embedding per public BB ID. Public IDs only.
• golden_predictions.json: environment verification fixture: format, seed 0, model_file, tolerance 2e-6, note, entries (20 fixed fold-0 recipes across 5 contexts, each {context, public_compound_id, recipe: {bb0..bb4}, prediction_usage_z_scored: [32 floats]}).
• model_def.py: self-contained architecture (PyTorch only).
• predict.py: CPU inference CLI and --check-golden self-check.

annex_imaging/

Full narrative: annex_imaging/README.md. All per-compound tables key on public_compound_id.

• zel024_compound_embeddings.parquet (10,129 × 3,075) / zel031_compound_embeddings.parquet (8,443 × 3,075): public_compound_id, n_fovs (fields of view averaged), n_panels, rn50_0000…rn50_2047 (ResNet-50 embedding means), clip_0000…clip_1023 (CLIP embedding means).

• zel024_compound_intensity.parquet (10,129 × 8): public_compound_id, n_fovs, n_panels, brightfield_cell_mean, dapi_cell_mean, p21_cell_mean, p62_cell_mean, phalloidin_cell_mean.

• zel031_compound_intensity.parquet (8,443 × 8): same key and support columns; marker set brd4_cell_mean, brightfield_cell_mean, cona_cell_mean, dapi_cell_mean, phalloidin_cell_mean. The two libraries are different panels (p62 only in zel024; BRD4/ConA only in zel031): pool only with panel-aware normalization.

• zel039_imaging_latents.parquet (78,896 detections × 457): public_compound_id (empty for unmapped detections; see mapped_to_compound_master), public_bb0_id…public_bb2_id, mapped_to_compound_master (bool), zlibrary, cell_line_context, scantype (cell painting or immunofluorescent staining), if_target (immunofluorescence target where applicable), D0…D447 (per-detection 448-dimensional image latents).

• reliability/embedding_reliability.json: min_fovs, n_splits, libraries (per library: n_fovs, n_compounds, and per backbone rn50/clip: split_half_sb_median_raw, null_median_raw, split_half_sb_median_centered, null_median_centered, n_compounds_evaluated). Split-half = two half-averages of a compound's FOVs correlated across compounds, Spearman-Brown corrected.

• reliability/marker_reliability.json: libraries (per library: n_fovs, n_compounds, markers with per-marker split_half_sb; plus the marker-marker correlation matrix).

• image_to_program_predictions/{context}/ and chemistry_to_program_predictions/{context}/: fold-clean test predictions, one .npz per fold × seed × model. NPZ keys:

  • public_compound_id: (n,) unicode strings, held-out test compounds only.
  • y_true_z: float32 (n, 32), measured usages, z-scored per program.
  • y_pred_z: float32 (n, 32), predicted usages, same z-scored space.

  Filename grammar: {features}_{model}__fold{f}_seed{s}.npz for image→program (features clip, rn50 for zel024/zel031; cp448, if448, mean448, scantype896 for zel039; models ridge, mlp), and fold{f}_seed{s}.npz for chemistry→program (a cross-attention model over the building blocks). Contexts covered: zel024_hek293, zel031_a549, zel031_thp1, zel039_aec7. Column j of either matrix is program Pj+1 of the pinned basis.

• prediction_score_summary.csv (175 rows): family (image_to_program / chemistry_to_program), context, model, fold, seed, n_test_compounds, mean_program_pearson. Derived from the dumps; recomputing a row is shown in examples/04_join_imaging.ipynb.

• decomposition.csv (128 rows: 4 contexts × 32 programs): context, program (P01-P32), image_features, chemistry_model, folds, n_test_compounds, r2_chem, r2_img, r2_joint, chem_only, img_only, shared, unexplained, img_only_fold_sd, chem_only_fold_sd, img_only_null_mean, img_only_null_sd, img_only_z, img_only_tail, chem_only_null_mean, chem_only_null_sd, chem_only_z, chem_only_tail, rel_img, img_only_rel_ceiling, r2_img_rel_ceiling, r_ceiling_img. Per-program variance ownership (chemistry alone / imaging alone / shared / neither) with permutation nulls and reliability ceilings.

• decision_grade_image_to_program_panels.csv (35 rows): context, image_backbone (clip / rn50), model (ridge / mlp), fold, seed, n_train_compounds, n_test_compounds, prog_pearson_mean, prog_pearson_median, pairing_null_mean, pairing_null_sd, pairing_null_z, pairing_null_tail_frac. Cell-level decision-grade image→program evaluation for zel024_hek293, zel031_thp1, zel031_a549.

• decision_grade_image_to_program_zel039_aec7.csv (60 rows): context, model_variant (mean448/scantype896 × ridge/mlp), fold, seed, train/val/test counts, prog_pearson_mean/median/min/max, null_mean/sd/q95/tail_frac_ge, above_floor_q95, margin_over_q95, model_hyperparameter. The 5-fold × 3-seed × 4-variant decision-grade zel039 evaluation.

• decode_through_comparison.csv (30 rows): context, fold, seed, train/test counts, bottleneck_mcpearson_* (decode through the 32-program bottleneck), direct_mcpearson_* (direct image→gene prediction), oracle_mcpearson_* (usage-space ceiling), null q95s, paired diff_mean with CI, fraction_of_oracle_filled.

• marker_program_association.csv (768 rows): context, library, marker, program, n_test, r_raw, z_raw, tail_raw, r_partial, z_partial, tail_partial, marker_rel_sb, r_ceiling, r_chem_pred, z_chem_pred, r_img_pred, z_img_pred, fw_flag. Marker intensity vs measured usages (r_raw; r_partial removes the shared cell-density axis) and vs image-predicted / chemistry-predicted usages, with nulls and reliability ceilings.

• marker_program_depth_stratified.csv (3 rows): context, marker, program, depth_stratum, n_test, r_partial, r_raw.

annex_hypotheses/

Read annex_hypotheses/README.md and HOW_TO_READ.md first. All tables are hypothesis-generating.

• anchor_leads.csv (16 rows): hypothesis_id (ZSH-####, stable), bb_level_ids, contexts, biological_program, matched_control, moa_class, external_triangulation, evidence_summary, confidence_tier (A/B/C), caveats (controlled vocabulary: weak_null, singleton, triage_grade, low_coverage), kill_confirm_experiment.
• program_atlas.csv (26 rows): program_group_id, context, primary_program, n_programs_merged, member_programs, theme_family, named_theme, top_genes, top_gene_sets, driving_bb_levels, n_bb_levels_q05, anchor_control, anchor_top3_controls, crispr_ko_links, member_compounds_usageZ_ge1, mean_abs_usage, frac_deep_top500, library_frac_deep, depth_tier_skew_flag, ica_median_abs_cos, confidence_tier, focus_experiment. Program-atlas labels use the namespace documented in the interpretation notes; they must not be used directly as current basis indices.
• sharp_sar_candidates.csv (133 rows): candidate_id, context, candidate_kind, n_members, within_phenotype_coherence, coherence_null_z, coherence_permutation_p, falloff_null_percentile, chemistry_within, ring1_similarity, driver_genes, evidence_tier (zel024 rows marked grammar_level), selection_note.
• hypothesis_ledger_full.csv (1,027 rows, triage-grade, hypothesis-generating): hypothesis_id, hypothesis_type, status (hypothesis_anchor_validated / hypothesis_strong / hypothesis_triage), confidence_tier, context, bb_level_ids, claim, evidence, matched_control, moa_class, caveats, evidence_rank_score.

Current named-control target annotations and their primary citation are documented in annotations.

annex_phenomimicry/

Read annex_phenomimicry/README.md first. These are hypothesis-grade compound × CRISPR-knockout correspondence tables. Named-control validation queries include random-target and hub-matched empirical comparisons; consensus scores and family rankings have separate definitions.

• phenomimic_pairs.parquet (426,461 rows): public_compound_id, target_gene, n_contexts, contexts, best_z, mean_z, best_rank, mean_rank, is_named_control, kd_delta_median, kd_flag_weak, tier (B_consensus_strong / B_single_context / C_moderate), bb0-bb4, hub_flag.
• antimimic_pairs.parquet (561,355 rows): direction-adjusted opposition track, selected by stored best_z >= 5. Context scores are the negative of the signed consensus before maximum/mean aggregation; negative standardized consensus does not prove negative unstandardized cosine or biological rescue. Columns: public_compound_id, target_gene, n_contexts, contexts, best_z, mean_z, best_rank, mean_rank, is_named_control, kd_delta_median, kd_flag_weak, hub_flag. There is no tier and no bb0–bb4. Score convention, worked example and method.
• showcase_hits.csv (2,000 rows): per-dataset evidence columns (n_datasets_scored, n_datasets_top5pct, n_datasets_top1pct, best_percentile, max_cosine, median_percentile, best_dataset_exact) plus consensus columns and control annotations (name, moa_class, primary_target).
• top100_phenomimics.csv (101 rows): rank, family_id, target_gene, family_size, family_max_z, family_evidence, public_compound_id, best_z, tier, contexts, kd_flag_weak, is_family_representative, n_contexts, family_contexts, total_family_size, hub_flag, kd_delta_median, mean_top3_z, replication_factor, composite_score.
• top100_family_summary.csv (31 rows): family_id, target_gene, family_size, family_max_z, family_evidence, n_in_top100, total_family_size, n_contexts, family_contexts, hub_flag, kd_delta_median, mean_top3_z, replication_factor, composite_score.
• validation_empirical_p.csv (2,074 rows): context, dataset, public_compound_id, target_gene, cosine, rank_mimic, n_targets, n_shared_genes, percentile, control_name, p_emp, p_emp_hub, p_emp_20draw.
• ensemble_rescoring_panel.csv (42 rows): public_compound_id, compound_name, target_gene, ens_min_best_percentile, cosine_best_percentile, frac_cells_top20, n_cells, consistent_pair. The per-pair table behind the ensemble-rescoring section of the annex README.
• target_hubness.csv (18,789 rows): target_gene, n_compounds_hit, hub_frac, hub_flag.

Additional records:

• family_null_check.csv: 100 degree-weighted draws plus six summary rows (draw, count_all_filtered, count_replicated_subset). There are 166 observed filtered families versus a null mean of 108.79 and SD of 9.44277. The replicated-subset count is zero in both the observed set and every draw (empirical p = 1). Null method and scope.
• curation_summary.json: current descriptive counts and known-control calibration summaries.

annex_chemistry/

Column-by-column definitions with interpretation are in annex_chemistry/README.md; headers for reference:

• novel_bb_generalization.csv (8 rows): context, structure_readout_model_r_mean, structure_readout_model_r_sd, identity_readout_model_r_mean, structure_minus_identity_r_mean, structure_minus_identity_r_ci95, structure_minus_identity_frac_folds_positive, reference_trunk_model_r_mean, structure_minus_trunk_r_mean, structure_ridge_r_mean, additive_embedding_floor_r_mean, structure_ridge_minus_floor_r_mean, structure_ridge_minus_floor_r_ci95, structure_ridge_minus_floor_frac_folds_positive.
• attribution_certificate.csv (6 rows): context, fold, usage_target, structure_model_r, model_null_z, n_building_blocks, bb_knockout_vs_measurement_median_spearman, bb_knockout_null_mean, bb_knockout_null_sd, bb_knockout_z, parts_sum_vs_direct_median_spearman, n_structure_parts, negative_control_median_spearman (empty where not applicable).
• activity_cliffs.csv (450 rows): context, bb_pair_id, n_members, within_phenotype_coherence, within_candidate_chemical_coherence, ring1_phenotype_correlation, ring2_phenotype_correlation, ring3_phenotype_correlation, falloff, null_falloff_mean, null_falloff_sd, falloff_null_percentile.
• chemotype_series.csv (2,727 rows): context, evidence_level (measured / grammar_level), bb_pair_id, position_a, bb_id_a, position_b, bb_id_b, n_members, effect_strength_l2, top20_gene_energy_fraction, participation_ratio, coherence, sibling_max_cos, sibling_mean_cos, n_siblings, null_coherence_mean, null_coherence_sd, coherence_z_vs_null, coherence_p_empirical, coherence_q_fdr_bh, effect_strength_z_vs_null, effect_strength_p_empirical, coherence_p_permutation, mean_shrinkage_weight, mean_n_wells, composite_score, composite_rank, is_positive_control, driver_genes_up, driver_genes_down, and per-depth-tier fractions (frac_*_depth_tier, library_base_frac_*_depth_tier, delta_frac_*_depth_tier).
• bb_effect_rankings.csv (431 rows): context, position, bb_id, n_carriers, in_sar_window, coherence, coherence_z, coherence_p, sparse_coherence, sparse_coherence_z, sibling_max_cos, sibling_max_sparse_j, sparse_falloff, effect_strength_l2, top20_gene_energy_fraction, delta_frac_deep_depth_tier, driver_genes_up, driver_genes_down, is_positive_control, sparse_composite_score, coherence_p_permutation, sparse_rank, within_candidate_chemical_coherence, ring1_phenotype_correlation, ring3_phenotype_correlation, falloff, falloff_null_percentile.

annex_same_well/

Full narrative: annex_same_well/README.md. The well key is (batch_id, well_id); well_id is unique only within a batch. Context label: same_well_hek293.

• same_well_wells.parquet (11,435 × 486): batch_id (batch_1 / batch_2), well_id, public_compound_id, control_name, cell_line, n_detections, img_lat_0_0…img_lat_3_63 (448 image latents, mean over the well's detections; blocks 0-2 × 128 plus block 3 × 64), D00…D31 (32 RNA latents, constant within a well).

• same_well_detections.parquet (14,757 × 486): the detection-level table behind the wells table: same columns plus detection_index (0-based within each well); image latents are per-detection, RNA latents repeat per well.

• control_compound_map.csv (35 rows): control_name, public_compound_id, public_compound_name.

• evidence/cross_modal_regimes.csv (3 rows): regime, n_wells, alpha, mcPearson, R2_mean_perdim, R2_pooled, null_mean, null_std, null_p95, p_value. Image→RNA ridge predictability in three regimes (raw / within-control / across-control LOGO).

• evidence/per_control_coupling.csv (36 rows: 35 controls plus a GLOBAL_WITHIN_CONTROL summary row): control, n_wells, pls1_corr, null_mean, null_std, null_p95, p_value.

• evidence/learning_curve.csv (6 rows): n_wells, mcPearson_mean, mcPearson_std, null_mean, null_std, null_p025, null_p975.

• evidence/learning_curve.png: the learning curve, plotted.

• evidence/mantel_bootstrap.csv: two resampling summaries with estimate, mantel_r, perm_p, boot_ci_lo, boot_ci_hi, boot_mean, boot_std. The Mantel r correlates pairwise correlation distances between 35 control means in image and RNA space (r = 0.77289; two-sided permutation p = 1/2,001). Controls and wells are different bootstrap units; the well bootstrap samples image and RNA wells independently within fixed controls. Methods and runnable check.

annex_clusters/

Read annex_clusters/README.md first.

• cluster_census.csv (1,007 rows): context, cluster_id, n_members, coherence, coherence_z, coherence_q, anchor_bb, anchor_pos, anchor_frac, top_up_set, top_up_q, top_dn_set, top_dn_q, prior_series_n (count of matching chemistry-series records by context and anchor building block). One row per chemistry-constrained response-family cluster recovered from the 32-program usage vectors; every row at coherence q <= 0.01 against 200 size-matched random compound sets.

• pathway_family_counts.csv: current pathway-family totals across the 1,007 census rows; 855 have a significant annotation and 152 do not. Selection and grouping rule.

annotations/

Control target annotations records current targets, mechanisms and primary citations. Annotation guide explains the MSC1094308 VCP/p97 and VPS4B annotation and the distinction between chemical response similarity and direct target evidence.

figures/

Figure guide indexes all six overview assets. platform_counts.json stores the five overview counts. measured_and_nominal_design_space.csv has one row per context: observed_compounds, bb0–bb4 vocabulary sizes, nominal_independent_combinations, observed_to_nominal_fraction, and scope. PNG/SVG pairs visualize those inputs. The results reference explains the distinct library-union calculation.

Additional pilot objects

Module	Principal key and unit	Schema/method entry
Response-family members/centroids	context, cluster_id and explicit atlas_id; member order and centroid row are separate	Response-family atlas
Selected case-study evidence	Chemical family/group; objective, policy, start and round; processed summaries	Case-study data guide
Measurement-design evidence	Exact well key or context/control/batch; sample/partition IDs and training size	Measurement design
Microscopy examples	Image crop index, channel and public compound ID; RNA linked by compound	Gallery

The annex index distinguishes measured profiles, predictions, correspondence analyses and hypotheses. Data access describes the processed-data scope and how to obtain raw measurements or external genetic references.