docs: add deep-research-verified dataset card

README.md

@@ -25,52 +25,202 @@ pretty_name: "Severson 2019 LFP Fastcharge — Tier 1 Raw Mirror"
 
 # severson-2019-raw
 
-**Tier 1 raw mirror** of the Severson et al. 2019 LFP fastcharge cycling
-dataset, hosted under the [BSEBench](https://bsebench.org) organization for
-reproducibility purposes. **Files are preserved exactly as published.**
+**Tier 1 raw mirror** of the Severson et al. 2019 commercial LFP fastcharge
+cycling dataset, hosted under the [BSEBench](https://bsebench.org)
+organization on the HuggingFace Hub. The files in this repository are
+preserved bit-exact as published on the original Toyota Research Institute
+data portal at [data.matr.io](https://data.matr.io/1/). No values are
+modified; no columns are renamed; no rows are dropped. Every file's
+SHA-256 digest is recorded in the BSEBench manifest YAML and matches the
+original distribution.
+
+This repository exists for **provenance verification and audits only**.
+For the BSEBench-canonical Parquet harmonization that consumers actually
+use for filter benchmarking, see the Tier 2 sibling repository
+[`bsebench-org/severson-2019`](https://huggingface.co/datasets/bsebench-org/severson-2019).
 
 ## Status
 
-🚧 **Upload pending** — placeholder dataset card. The raw `.mat` files will
-be uploaded once verified for SHA-256 integrity against the original
-publication.
+This is a **placeholder card**. The raw `.mat` files are not yet uploaded
+to the HuggingFace Hub. The planned upload pipeline is :
+
+1. Manual download of the three Severson batches from
+   `https://data.matr.io/1/projects/5c48dd2bc625d700019f3204` (registration
+   may be required by the TRI portal).
+2. Local SHA-256 computation via `scripts/upload_tier1_to_hf.py --src ./local
+   --repo-id bsebench-org/severson-2019-raw --private --dry-run`.
+3. Inventory cross-check against
+   `bsebench-datasets/manifests/severson_2019_lfp.yaml` (committed only after
+   real digests are populated — no fake checksums in this repository).
+4. Public upload (`--dry-run` removed) once the manifest validates.
+5. Update of this card with the populated `## File inventory` section, the
+   manifest commit SHA, and a `verified_at` timestamp.
+
+Until step 5 is reached, treat the file inventory below as a **best-effort
+estimate** based on community references (BatteryML, BEEP, MIT Braatz Group
+GitHub repository), not as a directly verified manifest of HuggingFace
+content.
 
 ## What this is
 
 A bit-exact mirror of the dataset published with :
 
-> **Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z.,
-> Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., Bazant, M. Z.,
-> Harris, S. J., Chueh, W. C., Braatz, R. D. (2019).**
+> **Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B.,
+> Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D.,
+> Bazant, M. Z., Harris, S. J., Chueh, W. C., Braatz, R. D. (2019).**
 > "Data-driven prediction of battery cycle life before capacity degradation."
-> *Nature Energy*, 4, 383-391. doi:[10.1038/s41560-019-0356-8](https://doi.org/10.1038/s41560-019-0356-8)
+> *Nature Energy*, **4**(5), 383–391.
+> doi:[10.1038/s41560-019-0356-8](https://doi.org/10.1038/s41560-019-0356-8)
+
+```bibtex
+@article{severson2019datadriven,
+  author  = {Severson, Kristen A. and Attia, Peter M. and Jin, Norman
+             and Perkins, Nicholas and Jiang, Benben and Yang, Zi
+             and Chen, Michael H. and Aykol, Muratahan
+             and Herring, Patrick K. and Fraggedakis, Dimitrios
+             and Bazant, Martin Z. and Harris, Stephen J.
+             and Chueh, William C. and Braatz, Richard D.},
+  title   = {Data-driven prediction of battery cycle life before
+             capacity degradation},
+  journal = {Nature Energy},
+  volume  = {4},
+  number  = {5},
+  pages   = {383--391},
+  year    = {2019},
+  doi     = {10.1038/s41560-019-0356-8},
+  url     = {https://www.nature.com/articles/s41560-019-0356-8},
+}
+```
+
+## Cell specifications
+
+| Property | Value |
+|---|---|
+| Manufacturer | A123 Systems |
+| Model | APR18650M1A |
+| Form factor | 18650 cylindrical |
+| Cathode chemistry | LFP (lithium iron phosphate, LiFePO4) |
+| Anode chemistry | Graphite |
+| Nominal capacity | 1.1 Ah |
+| Nominal voltage | 3.3 V |
+| Charge cutoff (used) | 3.6 V |
+| Discharge cutoff (used) | 2.0 V |
+| Number of cells (this dataset) | 124 |
+| End-of-life threshold | 80 % capacity retention |
+
+## Cycling protocol
+
+All cells were cycled inside a 30 °C controlled environmental chamber.
+Charging was performed under one-step or two-step fast-charging policies
+spanning charge rates from 1C to 6C (corresponding to 8 to 13.3 minutes
+to reach 80 % SOC), giving a total of 72 distinct fast-charging strategies
+across the cohort. Discharging was uniform : 4C constant-current to the
+discharge cutoff. A 1-minute rest was enforced after reaching 80 % SOC
+during charging, and a 1-second rest after each discharge. Internal
+resistance was probed once per cycle by 10 pulses of ±3.6C with a pulse
+width of 30 or 33 ms.
+
+This protocol is what makes Severson 2019 a strong stress-test for filter
+benchmarks : the cell-to-cell variation is dominated by *charging policy*
+rather than ambient conditions, isolating the protocol-driven aging
+mechanisms that filters are typically asked to compensate for.
+
+## File inventory (best-effort)
+
+Severson 2019 is distributed as **three** `.mat` files (HDF5 v7.3 format)
+on the TRI data portal :
+
+| File | Date | Cells | Size (approx.) |
+|---|---|---|---|
+| `2017-05-12_batchdata_updated_struct_errorcorrect.mat` | 2017-05-12 | 46 | 2.82 GB |
+| `2017-06-30_batchdata_updated_struct_errorcorrect.mat` | 2017-06-30 | 48 | 1.80 GB |
+| `2018-04-12_batchdata_updated_struct_errorcorrect.mat` | 2018-04-12 | 46 | 3.01 GB |
+| **Total** | | **140 channels → 124 cells after exclusions** | **~7.6 GB** |
+
+A fourth file dated 2019-01-24 is sometimes seen in the same data.matr.io
+project ; that file belongs to **Attia et al. 2020**
+("Closed-loop optimization of fast-charging protocols for batteries with
+machine learning") and is **not** part of Severson 2019. This Tier 1
+mirror covers only the three Severson 2019 batches.
 
-124 commercial LFP/graphite cells (A123 APR18650M1A, 1.1 Ah nominal) cycled
-under 72 fast-charging protocols at 30 °C, until 80 % capacity retention.
+The 16 channels that account for the gap between the 140 raw channels and
+the published cohort of 124 cells are documented in the upstream Braatz
+Group `Load Data.ipynb` notebook : five cells in batch 1 did not reach
+the 80 % capacity threshold (`b1c8`, `b1c10`, `b1c12`, `b1c13`, `b1c22`),
+five cells in batch 2 were re-assigned to batch 1 because they were
+continued from the first experimental run (`b2c7`, `b2c8`, `b2c9`,
+`b2c15`, `b2c16`), and six cells in batch 3 were excluded as noisy
+channels (`b3c37`, `b3c2`, `b3c23`, `b3c32`, `b3c42`, `b3c43`). The Tier 1
+mirror still preserves these channels in the raw `.mat` files ; the Tier 2
+canonical Parquet repository will apply the published exclusion mask.
+
+Sizes are rounded community estimates (see BatteryML and the BatteryBits
+"Comparison of Open Datasets for Lithium-ion Battery Testing" article).
+Exact bytes will be locked once the actual upload to HuggingFace
+completes and `manifests/severson_2019_lfp.yaml` is populated with
+SHA-256 digests.
 
 ## Why "raw mirror" tier
 
 BSEBench follows a **dual-tier** dataset strategy :
 
-- **Tier 1 (this repo)** — original `.mat` files, SHA-256 verified vs the
-  original publication. Used for **provenance verification** and audits.
-- **Tier 2** — see [`bsebench-org/severson-2019`](https://huggingface.co/datasets/bsebench-org/severson-2019)
-  for the **harmonized BSEBench-canonical Parquet** with consistent column
-  names, BPX-1.1 sign convention, and unified schema.
-
-For most filter benchmarking work, you want **Tier 2**. Tier 1 exists to
-let auditors verify our harmonization is faithful to the original.
+- **Tier 1 (this repository)** — the original `.mat` files, preserved
+  byte-for-byte, with SHA-256 digests recorded in our manifest and
+  cross-checked against the original publication's distribution.
+  Use this tier if you need to verify provenance, run independent
+  harmonizations, or audit our adapter's correctness.
+- **Tier 2** — the BSEBench-canonical Parquet harmonization at
+  [`bsebench-org/severson-2019`](https://huggingface.co/datasets/bsebench-org/severson-2019).
+  Consistent column names, BPX-1.1 sign convention, unified schema across
+  all benchmark datasets. Use this tier for filter benchmarking and most
+  downstream work.
 
 ## Original source
 
-The original publication was distributed via [data.matr.io](https://data.matr.io/1/)
-(MIT/Stanford battery archive). This URL is recorded as **citation
-metadata** but BSEBench's HuggingFace mirror is the **single source of
-truth** for fetching — independent of `data.matr.io`'s long-term availability.
+The original Severson 2019 dataset was distributed via
+[data.matr.io](https://data.matr.io/1/) (the Toyota Research Institute
+Experimental Data Platform), specifically project
+[`5c48dd2bc625d700019f3204`](https://data.matr.io/1/projects/5c48dd2bc625d700019f3204).
+This URL is recorded as **citation and provenance metadata** only.
+
+**The HuggingFace Hub mirror at this repository is the BSEBench
+single source of truth for fetching.** Adapters in
+`bsebench-datasets` never hit `data.matr.io` at runtime. This insulates
+the benchmark from upstream availability changes (URL shifts, registration
+requirements, bandwidth limits, eventual portal retirement) while
+preserving the citation chain back to the original publishers.
 
 ## License
 
-[Creative Commons Attribution 4.0 International (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/) — same as the original publication.
+The Severson 2019 dataset is distributed under the
+[Creative Commons Attribution 4.0 International (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/)
+license, consistent with the licensing policy of the `data.matr.io`
+platform's earlier (pre-2025) datasets per the
+[TRI Energy & Materials Datasets](https://data.matr.io/) catalog.
+
+Verbatim core grant from the
+[CC-BY-4.0 legal code](https://creativecommons.org/licenses/by/4.0/legalcode) :
+
+> "Subject to the terms and conditions of this Public License, the
+> Licensor hereby grants You a worldwide, royalty-free, non-sublicensable,
+> non-exclusive, irrevocable license to exercise the Licensed Rights in
+> the Licensed Material to: (1) reproduce and Share the Licensed Material,
+> in whole or in part; and (2) produce, reproduce, and Share Adapted
+> Material."
+
+The redistribution rights granted by this license are the legal basis on
+which BSEBench mirrors the dataset on the HuggingFace Hub. Attribution
+is given to the original authors via the BibTeX block above and via the
+manifest's `citation_bibtex` field. Derivative material (the Tier 2
+Parquet harmonization at `bsebench-org/severson-2019`) is offered under
+the same CC-BY-4.0 license, with BSEBench attribution added on top of
+the original Severson 2019 attribution chain.
+
+Note : the *publication text* of the Nature Energy paper is governed by
+Springer-Nature's text-and-data-mining terms (CrossRef license type
+`tdm`, effective 2019-03-25), which is a separate licensing regime from
+the dataset hosted on data.matr.io. CC-BY-4.0 covers the experimental
+data only ; do not assume it covers the paper PDF.
 
 ## How to use
 
@@ -81,23 +231,43 @@ local_dir = snapshot_download(
     "bsebench-org/severson-2019-raw",
     repo_type="dataset",
 )
-# local_dir contains the raw .mat files, SHA-256 verified
+# local_dir contains the three .mat files, SHA-256 verified
+# against bsebench-datasets/manifests/severson_2019_lfp.yaml
+```
+
+To then read a `.mat` file in Python (the files are HDF5 v7.3, not
+classic v5, so `scipy.io.loadmat` will not work — use `h5py`) :
+
+```python
+import h5py
+from pathlib import Path
+
+p = Path(local_dir) / "2017-05-12_batchdata_updated_struct_errorcorrect.mat"
+with h5py.File(p, "r") as f:
+    print(list(f.keys()))                  # ['#refs#', '#subsystem#', 'batch', 'batch_date']
+    batch = f["batch"]
+    print(list(batch.keys()))               # ['Vdlin', 'barcode', 'channel_id',
+                                            #  'cycle_life', 'cycles', 'policy',
+                                            #  'policy_readable', 'summary']
 ```
 
-For the BSEBench-harmonized version with a consistent Python API, prefer
+For the BSEBench-harmonized Parquet version that exposes a clean
+benchmark-ready API, prefer
 [`bsebench-org/severson-2019`](https://huggingface.co/datasets/bsebench-org/severson-2019).
 
 ## Citation
 
-Cite the **original** Severson 2019 paper (BibTeX above) — not BSEBench's
-mirror. BSEBench's contribution is hosting + harmonization, not the data.
+Cite the **original** Severson 2019 paper (BibTeX above). BSEBench's
+contribution is hosting and harmonization, not the data itself.
 
-If you also use BSEBench tooling, additionally cite :
+If you also use BSEBench tooling for filter benchmarking, additionally
+cite :
 
 ```bibtex
 @misc{bsebench2026,
-  author = {Akir, Oussama and BSEBench Contributors},
-  title  = {{BSEBench}: an open-source benchmark for battery state-estimation filters},
+  author = {Akir, Oussama and {BSEBench Contributors}},
+  title  = {{BSEBench}: an open-source benchmark for battery
+            state-estimation filters},
   year   = {2026},
   url    = {https://bsebench.org},
 }
@@ -105,10 +275,31 @@ If you also use BSEBench tooling, additionally cite :
 
 ## Provenance manifest
 
-A machine-readable manifest validating this dataset's metadata against the
-[`bsebench-dataset-manifest/v1`](https://github.com/bsebench-org/bsebench-specs)
-schema lives at
+A machine-readable manifest validating this dataset's metadata against
+the [`bsebench-dataset-manifest/v1`](https://github.com/bsebench-org/bsebench-specs)
+Pydantic v2 schema lives at
 [`bsebench-org/bsebench-datasets/manifests/severson_2019_lfp.yaml`](https://github.com/bsebench-org/bsebench-datasets/tree/main/manifests).
 
-Including : `source.canonical_url`, `source.canonical_doi`, every file's
-SHA-256 + size, license, and citation BibTeX.
+The manifest records, for every `.mat` file in this repository :
+
+- `source.canonical_url` — the data.matr.io project URL
+- `source.canonical_doi` — the Nature Energy DOI for citation
+- `source.publication_authors` and `source.publication_year`
+- per-file `path`, `sha256`, and `size_bytes`
+- the dataset-wide `license` (SPDX `CC-BY-4.0`) and `redistribution_allowed`
+  flag (true)
+- the `citation_bibtex` block (verbatim copy of the BibTeX above)
+- `huggingface_tier1_repo` (= `bsebench-org/severson-2019-raw`) and
+  `huggingface_tier2_repo` (= `bsebench-org/severson-2019`)
+
+The manifest is committed only after the SHA-256 digests are populated
+from the actual HuggingFace mirror — never with placeholder values.
+
+## See also
+
+- [Tier 2 canonical Parquet sibling repository](https://huggingface.co/datasets/bsebench-org/severson-2019)
+- [Original publication (Nature Energy)](https://doi.org/10.1038/s41560-019-0356-8)
+- [Original data portal (TRI / data.matr.io)](https://data.matr.io/1/projects/5c48dd2bc625d700019f3204)
+- [Upstream Braatz Group GitHub starter code](https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation)
+- [BSEBench organization on HuggingFace](https://huggingface.co/bsebench-org)
+- [BSEBench documentation site](https://bsebench.org)