Card restructure: symmetric two-views design (no primary/secondary), shared Data Model + How-Built combined, forward-compatible 'a supplement' phrasing for survival_derived, trimmed disclaimer.

README.md

@@ -145,81 +145,81 @@ configs:
 
 # TCGA Patients (Open Access)
 
-Open-access TCGA data from the NCI Genomic Data Commons (GDC), reshaped as
-one HuggingFace (HF) subset per TCGA project; one row per patient. Covers
+Open-access TCGA data from the NCI Genomic Data Commons (GDC). Covers
 all 33 TCGA projects.
 
-- **Generated:** 2026-05-09 20:13:48 UTC
+**This view presents one HuggingFace subset per TCGA project**, with one row per patient. See the [`tcga-tabular-open`][tabular] companion for a per-table view of the same underlying data.
+
+- **Generated:** 2026-05-09 21:21:40 UTC
 - **Schema:** derived from the [GDC Data Dictionary][gdc-dict].
 - **GDC data release:** Data Release 45.0 - December 04, 2025
 
 ## Data model
 
-Closely follows the GDC data model — see the [GDC Data Dictionary][gdc-dict]
-for the canonical entity-by-entity definitions. Each row is one `case` (one
-patient) with the full biospecimen subtree:
-
-```
-case          one patient (TCGA-XX-1234)
-└── sample    physical specimen taken from the patient at one timepoint
-              (Primary Tumor, Solid Tissue Normal, Blood Derived Normal, ...)
-    └── portion    a piece of that sample for a specific lab process
-        └── analyte    extracted material of one type (DNA or RNA)
-            └── aliquot    a vial of that analyte handed off for sequencing
-```
-
-
-### Where this dataset deviates from the GDC
-
-The few places this row layout differs from a direct mapping of the GDC `case`
-tree:
-
-- **Top-level convenience columns.** Each row carries `gdc_portal_url`
-  (templated link to the patient's GDC Data Portal page) and
-  `samples_<gdc_data_type_snake_case>` molecular vectors (e.g.
-  `samples_masked_somatic_mutation`,
-  `samples_gene_expression_quantification`). These let consumers
-  column-project just the modalities they need; each entry carries foreign
-  keys (FKs) back to `samples[].portions[].analytes[].aliquots[]`.
-- **Resolved sample FKs on Mutation Annotation Format (MAF) rows.** The GDC
-  ships MAF variants with aliquot UUIDs in `Tumor_Sample_UUID` /
-  `Matched_Norm_Sample_UUID`; we additionally resolve those to
-  `tumor_sample_id` / `matched_normal_sample_id` so consumers can join
-  straight to `samples[]`.
-- **Stranded RNA-Seq columns dropped.** Each Gene Expression record drops
-  `stranded_first` / `stranded_second`; the GDC pipeline [harmonizes
-  RNA-Seq reads as
-  unstranded](https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/),
-  so `unstranded` is the canonical column.
+### Where the data comes from
 
-## How this dataset was built
-
-Every row originates in the NCI Genomic Data Commons (GDC). Three
-sources feed each project's data:
+Three sources feed each project's data, all open-access:
 
 - **Case-level clinical structure** — fetched from the GDC `/cases`
   endpoint, returning the full nested case JSON (demographic + diagnoses
   → treatments + follow_ups + exposures + family_histories + samples →
-  portions → analytes → aliquots).
+  portions → analytes → aliquots). The biospecimen subtree on each case:
+
+  ```
+  case          one patient (TCGA-XX-1234)
+  └── sample    physical specimen taken from the patient at one timepoint
+                (Primary Tumor, Solid Tissue Normal, Blood Derived Normal, ...)
+      └── portion    a piece of that sample for a specific lab process
+          └── analyte    extracted material of one type (DNA or RNA)
+              └── aliquot    a vial of that analyte handed off for sequencing
+  ```
+
 - **Per-modality molecular files** — discovered via `/files` (filtered
   by the clauses in the table below) and downloaded via `/data`. Each
-  combination locks one `data_type` to a specific GDC pipeline so future
-  GDC additions can't silently ship different content under the same
-  `data_type`.
-- **BCR Clinical Supplement files** — original Biospecimen Core Resource
-  (BCR) clinical biotab forms (per-project TSVs: patient, follow_up,
-  nte, drug, radiation, etc.). The harmonized `/cases` endpoint drops
-  or under-populates a number of clinical fields that the BCR-original
-  biotabs preserve — most importantly Liu et al. 2018's
-  `treatment_outcome_first_course`, used by `survival_derived`.
-  Discovered via `/files` filtered to `data_type="Clinical Supplement"`
-  + `data_format="bcr biotab"`, downloaded via `/data`.
-
-| Lands at | data_type | data_format | data_category | experimental_strategy | analysis.workflow_type |
-|---|---|---|---|---|---|
-| `samples_masked_somatic_mutation` array | `Masked Somatic Mutation` | `MAF` | `Simple Nucleotide Variation` | `WXS` | `Aliquot Ensemble Somatic Variant Merging and Masking` |
-| `samples_gene_expression_quantification` array | `Gene Expression Quantification` | `TSV` | `Transcriptome Profiling` | `RNA-Seq` | `STAR - Counts` |
-| consumed in-memory for `survival_derived` | `Clinical Supplement` | `bcr biotab` | `Clinical` | | |
+  combination locks one `data_type` to a specific GDC pipeline so a
+  future GDC addition can't quietly substitute a different pipeline
+  under the same `data_type`.
+- **BCR Clinical Supplement biotabs** — original Biospecimen Core
+  Resource (BCR) clinical forms shipped as per-project TSVs (one per
+  form: patient, follow_up, nte, drug, radiation, etc.). The harmonized
+  `/cases` endpoint drops or under-populates a number of clinical fields
+  the BCR-original biotabs preserve. The schema varies by cancer type
+  (e.g. BLCA's BCG-response columns don't exist in CHOL's hepatic-marker
+  forms), so each project's biotabs ship only the columns they actually
+  carry. Discovered via `/files` filtered to `data_type="Clinical
+  Supplement"` + `data_format="bcr biotab"`, downloaded via `/data`.
+
+### Source data filters (canonical)
+
+Same in both views of the dataset; each row locks the `/files` query
+for one source:
+
+| data_type | data_format | data_category | experimental_strategy | analysis.workflow_type |
+|---|---|---|---|---|
+| `Masked Somatic Mutation` | `MAF` | `Simple Nucleotide Variation` | `WXS` | `Aliquot Ensemble Somatic Variant Merging and Masking` |
+| `Gene Expression Quantification` | `TSV` | `Transcriptome Profiling` | `RNA-Seq` | `STAR - Counts` |
+| `Clinical Supplement` | `bcr biotab` | `Clinical` | | |
+
+### How each source appears in this view
+
+| Source | Where it lands |
+|---|---|
+| GDC `/cases` | nested fields on each patient row (`demographic`, `diagnoses`, `follow_ups`, `exposures`, `family_histories`, `samples`); `gdc_portal_url` link added |
+| Masked Somatic Mutation MAFs | `samples_masked_somatic_mutation` array on each patient row (sample FKs resolved alongside GDC's aliquot UUIDs) |
+| Gene Expression Quantification | `samples_gene_expression_quantification` array on each patient row (`stranded_first` / `stranded_second` dropped — GDC harmonizes as unstranded) |
+| BCR Clinical Supplements | consumed in-memory for the `survival_derived` supplement (see below); not surfaced as columns on the row |
+
+
+### Specific to this view
+
+- Convenience: each row carries `samples_<modality>` array columns so
+  you can column-project just the molecular data you need without
+  walking the nested GDC entities.
+- Loading: the [`tcga2hf` package][repo] ships a typed `TcgaHfPatient`
+  pydantic model that mirrors this schema and adds convenience joins
+  (tumor/normal pairs, mutations-by-gene, expression-by-gene,
+  longitudinal timeline).
+
 
 ### Provenance pinned per build
 
@@ -232,17 +232,18 @@ sources feed each project's data:
 See the [repository][repo] for full request payloads, filter clauses,
 and the build pipeline source.
 
+
 ## Survival endpoints (`survival_derived`)
 
-The only value-added enrichment in this dataset. Four standard survival
-endpoints — Overall Survival (OS), Disease-Specific Survival (DSS),
-Progression-Free Interval (PFI), Disease-Free Interval (DFI) — re-derived
-from the current GDC data using the algorithm from **Liu et al. 2018**
+We have provided a supplement to the GDC source data: re-derived
+survival endpoints — Overall Survival (OS), Disease-Specific Survival
+(DSS), Progression-Free Interval (PFI), Disease-Free Interval (DFI) —
+following the algorithm published by **Liu et al. 2018**
 ([DOI 10.1016/j.cell.2018.02.052](https://doi.org/10.1016/j.cell.2018.02.052)).
 
-Each patient row carries a top-level **`survival_derived` struct** with eight sub-fields: `os_event` / `os_time`, `dss_event` / `dss_time`, `pfi_event` / `pfi_time`, `dfi_event` / `dfi_time`. `*_event` is 0/1 (event observed vs censored); `*_time` is days
-from `index_date` (TCGA: diagnosis date). DFI is null for SKCM / THYM /
-UVM / LAML — Liu specifies no DFI for those tumor types.
+Each patient row carries a top-level **`survival_derived` struct** with eight sub-fields: `os_event` / `os_time`, `dss_event` / `dss_time`, `pfi_event` / `pfi_time`, `dfi_event` / `dfi_time`. `*_event` is 0/1 (event observed vs censored); `*_time` is
+days from `index_date` (TCGA: diagnosis date). DFI is null for SKCM /
+THYM / UVM / LAML — Liu specifies no DFI for those tumor types.
 
 We've reimplemented Liu's method against the current TCGA data and find
 broad agreement with the original curated CDR. Differences exist and are
@@ -253,25 +254,23 @@ migrations all contribute to the gap. This work is evolving; see the
 methodology.
 
 **Why we don't ship Liu's curated 2018 values directly:** the CDR is a
-frozen 2018 snapshot derived from a since-modified GDC release. Including
-those values would lock in irreproducible source-data drift. We re-derive
-on every build, so the values reflect the current GDC and are reproducible
-from this dataset's other tables alone.
+frozen 2018 snapshot derived from a since-modified GDC release.
+Including those values would lock in irreproducible source-data drift.
+We re-derive on every build, so the values reflect the current GDC and
+are reproducible from this dataset's other tables alone.
 
 ## Loading
 
-The [`tcga2hf` package][repo] ships a typed `TcgaHfPatient` pydantic model
-that mirrors this schema and adds convenience joins (tumor/normal pairs,
-mutations-by-gene, expression-by-gene, longitudinal timeline).
-
 ```python
-import pyarrow.parquet as pq
-from tcga2hf.models import TcgaHfPatient
+from datasets import load_dataset
 
-t = pq.read_table("TCGA-CHOL/data.parquet")
-patients = [TcgaHfPatient.model_validate(r) for r in t.to_pylist()]
+# One config per TCGA project.
+luad = load_dataset("gabrielaltay/tcga-patients-open", "TCGA-LUAD")
 ```
 
+Each row is one patient with the full GDC `case` structure nested
+in-place plus the `survival_derived` struct.
+
 ## GDC references
 
 - [Data dictionary][gdc-dict] (every entity + field definition)
@@ -332,11 +331,9 @@ Policy references:
 
 **This project is not affiliated with the NCI, GDC, or the TCGA Research
 Network.** It is an experimental open-source pipeline that may change
-significantly between versions; pin the GDC release and dataset commit
-if you depend on a specific snapshot. Re-derive from the GDC for any
-analysis where freshness matters — the dataset reflects the GDC release
-pinned in each project's `gdc_status.json`. Pipeline source:
-[`galtay/tcga2hf`][repo].
+significantly between versions. Pipeline source: [`galtay/tcga2hf`][repo].
 
 [gdc-dict]: https://docs.gdc.cancer.gov/Data_Dictionary/
 [repo]: https://github.com/galtay/tcga2hf
+[patients]: https://huggingface.co/datasets/gabrielaltay/tcga-patients-open
+[tabular]: https://huggingface.co/datasets/gabrielaltay/tcga-tabular-open