diff --git a/.gitattributes b/.gitattributes
index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..f0280cc01428612018992851ec24f3f140046f26 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+assets/dandi-000715-sub-56-YAaDV/reference.nwb filter=lfs diff=lfs merge=lfs -text
diff --git a/LICENSE-code.txt b/LICENSE-code.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2bb6ba12e9b1ea9ba7d95c98a4e7a07f6b70e773
--- /dev/null
+++ b/LICENSE-code.txt
@@ -0,0 +1,20 @@
+MIT License
+
+Copyright (c) 2026 WormTrack-Verify contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/README.md b/README.md
index 32897cd3e640101ba184f8c4ccd896981de3804a..effee896530a02e01b4f667490d6f4b4e24ad839 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,145 @@
 ---
-license: mit
+pretty_name: WormTrack-Verify (development pilot)
+license: other
+license_name: source-specific-data-licenses
+license_link: https://huggingface.co/datasets/pytc/trackingBench/blob/main/docs/LICENSING.md
+language:
+- en
+tags:
+- biology
+- microscopy
+- cell-tracking
+- c-elegans
+- verification
+- 3d
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: recordings
+  data_files:
+  - split: development
+    path: data/recordings/**/*.parquet
+- config_name: detections
+  data_files:
+  - split: development
+    path: data/detections/**/*.parquet
+- config_name: tracks
+  data_files:
+  - split: development
+    path: data/tracks/**/*.parquet
+- config_name: posture
+  data_files:
+  - split: development
+    path: data/posture/**/*.parquet
+- config_name: anatomy
+  data_files:
+  - split: development
+    path: data/anatomy/**/*.parquet
+- config_name: perturbations
+  data_files:
+  - split: development
+    path: data/perturbations/**/*.parquet
+- config_name: verification
+  data_files:
+  - split: development
+    path: data/verification/**/*.parquet
 ---
+
+# WormTrack-Verify — v0.1.0-alpha.1
+
+A public **development pilot** for testing anatomical verification of anonymous neuron trajectories in *C. elegans*. This is usable data and executable evaluation machinery. **The full initial benchmark and release gates A/B/C are not complete.** There is no frozen held-out test set in this release.
+
+## Motivation and scientific hypothesis
+
+Tracking errors contaminate neural activity measurements. Eutely supports persistent neuronal populations in adult hermaphrodites; it does not imply 302 visible detections or immutable positions. The hypothesis is that an independently estimated, restricted deformation model can predict neuronal positions with uncertainty sufficiently smaller than local inter-neuron spacing to expose identity errors. Four planar eigenworm modes do not establish a four-dimensional model of 3D internal tissue motion. The benchmark must allow this hypothesis to fail.
+
+## Inventory and actual included material
+
+| Component | Observed count / status |
+|---|---|
+| Recording-level registry | 264 |
+| Original WormID source assets | 118 |
+| ASCENT human-corrected observations | 105599 |
+| ASCENT anonymous tracks | 96 |
+| Hosted imaging excerpts | 44 volumes across four recordings |
+| Reference point clouds | 2 |
+| Independent behavior summaries | 6,400 samples; one SF acquisition |
+| Measured centerlines / eigenworm coefficients | 0; unavailable |
+| Frozen train/validation/test | Not released; development only |
+
+The five standardized tracking tables cover ASCENT, ZephIR, a NeRVE-derived recording redistributed in ZephIR, Targettrack, and a 16-frame WormID EY excerpt. Only ASCENT is currently eligible for the trusted tracking and corruption pilot. ZephIR scorer codes and Targettrack mask provenance are retained; unresolved annotations are **unknown**, not ground truth. The archive also contains a mouse experiment, which is excluded.
+
+All 36 WormID tracking acquisitions and 38 excluded SF recordings are mapped at inventory level. NP/HL structural recordings are not presented as dynamic tracking. [Assembly report](docs/ASSEMBLY_REPORT.md) distinguishes inventoried assets, inspected data, validated tables, and trusted correspondence labels. [Source audit](docs/SOURCE_AUDIT.md) records blocked resources.
+
+## Tracking versus verification
+
+`tracking_gt` isolates association on fixed source-supported objects and preserves the original WormID reference-frame protocol for future reproduction. `anatomical_verification` tests labeled identity corruptions. `anatomical_consistency` contains consistency diagnostics with incomplete labels. These protocols are distinct from train/validation/test partitions.
+
+The pilot provides two untrained oracle association baselines (Euclidean nearest neighbor and Hungarian assignment), six controlled error types, clean controls, and a fixed-reference-distance consistency baseline. Its threshold is a declared **5 micrometers**, chosen as a development demonstration, not calibrated to a guaranteed false-acceptance target. Reported results are descriptive development diagnostics. No full-image tracking, HOTA/IDF1, independent deformation fitting, or cross-laboratory generalization claim is made.
+
+## Data formats and coordinates
+
+Structured data are recording-partitioned Parquet. Original annotation HDF5 files and a small original structural NWB are retained where allowed. Imaging pilots use lossless chunked HDF5 with one CZYX dataset per hosted frame; source intensities are unnormalized. The logical volume axis order is TCZYX. This HDF5 representation is not advertised as OME-Zarr/NGFF.
+
+Point coordinates are named XYZ. Native voxel coordinates and transforms are preserved. Physical fields are null when calibration is unavailable. ASCENT spacing is (0.243, 0.243, 1.5) micrometers in XYZ, from the source record. WormID spacing comes from the inspected NWB. ZephIR normalized coordinates are converted through the source pixel-center convention; 17 out-of-bounds observations remain present with `validity=false`. Targettrack centroids remain in its source-processed image coordinates with an unresolved transform direction.
+
+## Ground-truth provenance
+
+ASCENT tracks were initialized with ZephIR and manually proofread/corrected by the source authors. This is **human-corrected algorithm output**, not independent de novo dense annotation. Source predictions live separately under `evaluation/source_predictions/` because their detections differ from oracle objects. Anonymous track IDs are sufficient; neuronal names are optional and are not used by the verifier.
+
+The WormID EY inspected source has 962 image frames and 961 segmentation frames. Only the available observations are published. No coordinate clamping, identity interpolation, or missing annotation fabrication is performed.
+
+## Anatomy and posture
+
+ASCENT's proofread first frame and a same-animal NeuroPAL structural cloud are included. Neither is a universal anatomical atlas. Unknown reference uncertainty is null. SF measured behavior summaries retain source timestamps and empty/unknown units. They are not centerlines, body coordinates, or a validated 3D deformation map. The client raises an explicit missing-component error for unsupported posture or imaging frames.
+
+## Splits and duplicates
+
+All included examples are assigned to `development`; train/validation/test are empty and no test performance is claimed. Original WormID fold values are preserved verbatim as a reproduction reference, not interpreted as this benchmark's partitions. The original split CSV has 120 rows and 118 unique filenames; two repeated structural entries are preserved in that reference table.
+
+Shared DANDI subject identifiers, particularly SK1 and DANDI 001623, are flagged as possible overlaps. Common animals/acquisitions and derivatives must stay together; unresolved overlap cannot be used to establish split disjointness. The NeRVE excerpt in ZephIR is not counted as an additional independent source acquisition. Test labels, when frozen in a later release, cannot calibrate corruptions or verification thresholds.
+
+## Access examples
+
+```python
+from datasets import load_dataset
+# Pin a commit SHA or the alpha tag after publication.
+registry = load_dataset("pytc/trackingBench", "recordings", revision="v0.1.0-alpha.1")
+tracks = load_dataset("pytc/trackingBench", "tracks", revision="v0.1.0-alpha.1", streaming=True)
+corruptions = load_dataset("pytc/trackingBench", "perturbations", revision="v0.1.0-alpha.1", streaming=True)
+print(next(iter(tracks["development"])))
+```
+
+The named configurations are `recordings`, `detections`, `tracks`, `posture`, `anatomy`, `perturbations`, and `verification`. The `posture` configuration currently contains **behavior summaries**, with explicit `component_type`, and zero measured centerlines. Loading tables does not download microscopy volumes. The recording registry includes inventory-only acquisitions; `hosted_tracks` and `hosted_volume` distinguish available components.
+
+For the bundled selective Python client, install this repository's small code subset:
+
+```python
+from huggingface_hub import snapshot_download
+import subprocess, sys
+code = snapshot_download("pytc/trackingBench", repo_type="dataset", revision="v0.1.0-alpha.1",
+                         allow_patterns=["src/**", "pyproject.toml", "README.md", "requirements.lock.txt"])
+subprocess.check_call([sys.executable, "-m", "pip", "install", code])
+from cell_tracking.data.wormtrack_verify import BenchmarkClient
+client = BenchmarkClient(revision="v0.1.0-alpha.1")
+annotations = client.load_tracks("ascent-opterra")
+volume = client.load_volume("ascent-opterra", frame=0)  # Small hosted excerpt only; CZYX.
+```
+
+Use [the executable pilot](examples/pilot.py) to reproduce the corruption and metric demonstration. Files larger than the client's 128 MiB budget require an explicit increased budget; no function silently downloads the benchmark or a multi-GB original movie. Original assets resolve through immutable DANDI asset IDs and versioned Zenodo records in `metadata/upstream_assets.json`. External assets are not Hub-hosted movies.
+
+## Licensing and attribution
+
+The original empty repository displayed MIT. That does not license third-party data. Included DANDI and Zenodo data are CC BY 4.0 per their release metadata. Benchmark helper code is MIT; source data retain their own licenses and attribution. Resources with unresolved redistribution rights remain references only. See [per-source licensing](docs/LICENSING.md), [catalog](docs/DATASET_CATALOG.md), and [citations](docs/CITATIONS.md).
+
+## Limitations and biases
+
+This pilot has only one trusted proofread tracking specimen, no measured centerlines, no independently calibrated deformation uncertainty, and no held-out evaluation. Unknown scorer codes are not promoted to manual ground truth. Fixed-reference residuals include legitimate tissue motion and are permutation-sensitive only because reference correspondences remain fixed. A static unordered point cloud cannot detect identity swaps. Acceptance by a geometric heuristic is not proof of biological identity.
+
+Coverage and selective risk use the full declared observation population; rejecting everything yields zero coverage. Sources differ in optics, visibility, annotation construction, and acquisition restrictions. The 302-neuron assumption is not universal across sex/stage and is never used to force observed counts.
+
+## Release history
+
+- `v0.1.0-alpha.1`: development pilot; source inventory, five standardized tracking tables, imaging excerpts, two reference clouds, measured behavior summaries, controlled corruptions, and reference baseline outputs. Full initial release gates remain open.
+
+Each release has a SHA256 assembly manifest, an upstream asset manifest, dependency pins, and machine-readable validation results. No model weights or externally pretrained models are used by these pilot baselines.
diff --git a/anatomy/README.md b/anatomy/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..5cf4543bb8f6846accfca9c2cc8456f673f90525
--- /dev/null
+++ b/anatomy/README.md
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+Reference point-cloud Parquet tables live in data/anatomy; no licensed population atlas has been redistributed.
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diff --git a/docs/ASSEMBLY_REPORT.md b/docs/ASSEMBLY_REPORT.md
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+# Assembly report — 0.1.0-alpha.1
+
+Generated from inspected assets, not publication counts.
+
+| Recording | Valid observations | Observed frames | Track IDs | Provenance |
+|---|---:|---:|---:|---|
+| ascent-opterra | 105599 | 1100 | 96 | {'human_corrected': 105599} |
+| zephir-zm9624 | 188663 | 1060 | 178 | {'unknown': 188663} |
+| nerve-al-w1-via-zephir | 121344 | 1536 | 79 | {'unknown': 119019, 'algorithm_prediction': 2325} |
+| targettrack-epfl10 | 1951 | 147 | 15 | {'unknown': 1951} |
+| dandi-000541-sub-20190924-01 | 2896 | 16 | 181 | {'unknown': 2896} |
+
+Registry: 264 source recordings; original WormID: 118 assets. These are not independent-animal counts. Imaging excerpts: 44 frames; references: 2 clouds; measured centerlines: 0. Exact verified observation/checkpoint coverage: ASCENT 105,599 human-corrected observations over 1,100 frames, 96 tracks. Other labels are not promoted to verified checkpoints.
+
+Release gates: {"A": "partial: full non-DANDI inventories pending", "B": "partial: independent moving-worm GT and centerline pending", "C": "not_passed: all P0 ingestion and held-out evaluation pending"}
+
+No full initial release gate is claimed. No test results, source-wide GT completeness, or cross-laboratory anatomical verification are claimed. Pilot diagnostic outputs and two oracle baseline predictions are in `evaluation/`.
+
+Source snapshots and asset digests pin upstream versions. Local workspace used `/tmp/wormtrack-verify-assembly` because the project filesystem quota rejected writes; public loading does not depend on that workspace.
diff --git a/docs/BENCHMARK_SPEC.md b/docs/BENCHMARK_SPEC.md
new file mode 100644
index 0000000000000000000000000000000000000000..8763fb90654843c816dbc7e46e2fe1d244d87885
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+++ b/docs/BENCHMARK_SPEC.md
@@ -0,0 +1,15 @@
+# Benchmark specification — development alpha
+
+Mission: tracking verification with anonymous persistent identities, independent anatomical evidence, explicit uncertainty, and the possibility of falsifying the deformation hypothesis. Auto-identification, calcium inference, and new tracking architectures are outside this assembly.
+
+Protocols: `tracking_gt`, `anatomical_verification`, `anatomical_consistency`. All current usable examples are development data, with empty train/validation/test partitions. Original WormID folds are preservation/reproduction metadata only. Future frozen partitions must be at physical-animal/provenance-group level and quarantine unresolved overlaps. Fit normalization, motion bounds and thresholds on training/calibration animals only. Never tune using hidden test labels.
+
+Oracle association uses fixed source detections. The pilot permits one recording-wide identity alignment through maximum overlap; never independent per-frame rematching. `correct_observation_rate` is globally aligned correct observed detections divided by all trusted reference observations. Missing predictions count against the fixed denominator. `identity_switches` counts changes in predicted identity along an observed reference trajectory. `fragmentations` counts identity runs or reappearance after missing predictions beyond the first run. These diagnostics are explicitly defined here, not canonical HOTA/IDF1 substitutes. Extra predicted observations are counted separately. Raw-image tracking is not evaluated in this alpha.
+
+Corruptions: one declared development seed 20261007; first anchor frame remains clean; start frame 300; single-frame swap, persistent swap, swap-and-recovery, nearby swap, fragmentation, and dropout. Recovery/nearby interval ends at 700; other persistent intervals end with the recording. Generic pairs use the declared random seed; the nearby-swap pair is chosen by nearest physical reference separation, on development data only. Preserve all original coordinates/detections/images; swaps change identity assignments, fragmentation creates a new ID, dropout toggles observation validity. Per-row error labels compare against the anchor identity, whereas the global-alignment association diagnostic optimizes one full-trajectory naming. Clean control rows are included and explicitly tagged.
+
+The pilot fixed-reference-distance verifier measures displacement in micrometers from the trusted same-animal first frame. It uses no candidate-specific refitting and assigns unsupported fragment identities to abstention. It also penalizes real tissue motion and is therefore a consistency diagnostic, not an independent deformation certificate. Threshold = 5 um declared as a demonstration; it is not a calibrated false-acceptance guarantee.
+
+Observation-level error AUROC/AUPRC use scikit-learn; prevalence is reported. Certified coverage = accepted observations / full fixed population. Selective risk = accepted labeled errors / all accepted observations (undefined if nothing accepted). False-certification rate = accepted labeled errors / all labeled errors (undefined without errors). Correct certified coverage = accepted correct observations / full population. Acceptance requires a finite score <= threshold. Risk-coverage ties enter together; unsupported observations retain their denominator and abstain. No independent-frame confidence intervals or pooled generalization estimates are reported for one specimen.
+
+Deferred full-benchmark work: source-provided dense/sparse GT audit across moving worms, physical uncertainty calibration, independent centerline/roll evidence, deformation prediction versus local spacing, additional corruption modes (smooth drift, duplicate identities and long-gap reassociation), canonical HOTA/IDF1 adapters with reference parity, cross-animal/lab/deformation splits, confidence intervals by experimental unit, and calibrated feasible-matching uniqueness. Eutely does not resolve swaps; unordered anatomy is permutation invariant; smooth registration fitted to candidate tracks cannot certify itself.
diff --git a/docs/CITATIONS.md b/docs/CITATIONS.md
new file mode 100644
index 0000000000000000000000000000000000000000..3a4a1651937c359213c5e8359d0d3032889b6465
--- /dev/null
+++ b/docs/CITATIONS.md
@@ -0,0 +1,17 @@
+# Source citations and immutable releases
+
+All source authors retain attribution. Complete author lists are in the linked official release metadata; this file does not invent missing bibliographic fields.
+
+- WormID/WormND benchmark: https://github.com/focolab/WormND/tree/0c74e9ddffee138cfc6330ab65c0e690e8e199dd and https://doi.org/10.1101/2025.01.06.631621 . Cohort-specific DANDI releases and attribution are in `metadata/sources.json` and `metadata/dandi_asset_provenance.json`.
+- Han et al., ASCENT: https://doi.org/10.1101/2025.07.23.666425 ; data https://doi.org/10.5281/zenodo.17561700 .
+- Torkashvand, Ryu, Venkatachalam: ZephIR data https://doi.org/10.5281/zenodo.10818810 ; code https://github.com/venkatachalamlab/ZephIR/tree/2842342b039c3aed8dab3b96b3ac0708e950e5b7 .
+- Nguyen et al. (2017), Automatically tracking neurons in a moving and deforming brain: https://doi.org/10.1371/journal.pcbi.1005517 ; original data https://doi.org/10.21227/H2901H .
+- Park et al., Targettrack: https://doi.org/10.1038/s41592-023-02096-3 ; data https://doi.org/10.5281/zenodo.10008744 .
+- Wu et al., CeNDeR: https://doi.org/10.1371/journal.pcbi.1010594 ; https://github.com/Wenlab/CeNDeR ; https://osf.io/v2b5n/ .
+- BrainAlignNet / ANTSUN 2.0: https://doi.org/10.7910/DVN/8UE0H9 ; metadata access incomplete, precise publication citation verify.
+- DANDI 001623 and 000981: pinned versions and official attribution in `metadata/sources.json` and `metadata/dandi_asset_provenance.json`.
+- Skuhersky anatomical atlas: https://github.com/bluevex/elegans-atlas/tree/65967d8abc38027ca69cdac18024052d168b8575 ; https://doi.org/10.1101/2021.06.09.447813 .
+- NeuroPAL, Yemini et al.: https://doi.org/10.1016/j.cell.2020.12.012 ; https://github.com/openworm/NeuroPAL/tree/85783437bea1112c1e4b1cacaac3e5337e7ce4a4 .
+- Statistical atlas: https://github.com/amin-nejat/stat-atlas/tree/29652ab83b6ff870b71a5969e25328fa8e488c90 ; precise paper citation verify.
+- OpenWorm Movement Database: https://movement.openworm.org/ ; access unavailable in audit.
+- Stephens, Johnson-Kerner, Bialek, Ryu (2008), Dimensionality and Dynamics in the Behavior of C. elegans: https://doi.org/10.1371/journal.pcbi.1000028 .
diff --git a/docs/DATASET_CATALOG.md b/docs/DATASET_CATALOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..7566157f262d743c20f78eafe973353a40c43234
--- /dev/null
+++ b/docs/DATASET_CATALOG.md
@@ -0,0 +1,24 @@
+# Dataset catalog (observed release metadata)
+
+| Source | Priority | Pinned version | Status | License | Bytes inventoried |
+|---|---|---|---|---|---|
+| [EY](https://dandiarchive.org/dandiset/000541/0.241009.1457) | P0 | 0.241009.1457 | inventoried_assets_not_bulk_ingested | ['spdx:CC-BY-4.0'] | 30525162874 |
+| [KK](https://dandiarchive.org/dandiset/000692/0.240402.2118) | P0 | 0.240402.2118 | inventoried_assets_not_bulk_ingested | ['spdx:CC-BY-4.0'] | 80537036658 |
+| [SK1](https://dandiarchive.org/dandiset/000565/0.241009.1504) | P0 | 0.241009.1504 | inventoried_assets_not_bulk_ingested | ['spdx:CC-BY-4.0'] | 46250221065 |
+| [SK2](https://dandiarchive.org/dandiset/000472/0.241009.1502) | P0 | 0.241009.1502 | inventoried_assets_not_bulk_ingested | ['spdx:CC-BY-4.0'] | 18442605394 |
+| [SF](https://dandiarchive.org/dandiset/000776/0.241009.1509) | P0 | 0.241009.1509 | inventoried_assets_not_bulk_ingested | ['spdx:CC-BY-4.0'] | 1011743300552 |
+| [NP](https://dandiarchive.org/dandiset/000715/0.241009.1514) | P0 | 0.241009.1514 | inventoried_assets_not_bulk_ingested | ['spdx:CC-BY-4.0'] | 835049434 |
+| [HL](https://dandiarchive.org/dandiset/000714/0.241009.1516) | P0 | 0.241009.1516 | inventoried_assets_not_bulk_ingested | ['spdx:CC-BY-4.0'] | 478075700 |
+| [Dunn](https://dandiarchive.org/dandiset/001623/0.251015.0312) | P1 | 0.251015.0312 | inventoried_assets_not_bulk_ingested | ['spdx:CC-BY-4.0'] | 774224141637 |
+| [Chemosensory](https://dandiarchive.org/dandiset/000981/0.260910.1747) | P1 | 0.260910.1747 | inventoried_assets_not_bulk_ingested | ['spdx:CC-BY-4.0'] | 82267067651 |
+| [ASCENT](https://zenodo.org/records/17561700) | P1 | 10.5281/zenodo.17561700 | pilot_assets_and_tables | cc-by-4.0 | 6253241706 |
+| [ZephIR](https://zenodo.org/records/10818810) | P0 | 10.5281/zenodo.10818810 | pilot_assets_and_tables | cc-by-4.0 | 14869755504 |
+| [Targettrack](https://zenodo.org/records/10008744) | P1 | 10.5281/zenodo.10008744 | pilot_assets_and_tables | cc-by-4.0 | 24089089928 |
+| [NeRVE original release](https://doi.org/10.21227/H2901H) | P0 | verify | one_annotation_recording_via_ZephIR; original_inventory_pending | verify | unknown |
+| [CeNDeR C1-C3](https://osf.io/v2b5n/) | P0 | verify | blocked_data_license_unstated; OSF_storage_empty; linked_Google_Drive_API_404 | verify | unknown |
+| [BrainAlignNet / ANTSUN 2.0](https://doi.org/10.7910/DVN/8UE0H9) | P1 | verify | Dataverse_API_unavailable; license_and_overlap_pending | verify | unknown |
+| [Skuhersky 3D anatomical atlas](https://github.com/bluevex/elegans-atlas) | P2 | verify | data_license_unresolved; external_reference_only | verify | unknown |
+| [NeuroPAL canonical positions](https://github.com/openworm/NeuroPAL) | P2 | verify | shared_Skuhersky_CSV; data_license_unresolved | verify | unknown |
+| [Statistical anatomical atlas](https://github.com/amin-nejat/stat-atlas) | P2 | verify | data_license_unresolved; external_reference_only | verify | unknown |
+| [OpenWorm Movement Database](https://movement.openworm.org/) | P2 | verify | DNS_resolution_failed; license_and_assets_unresolved | verify | unknown |
+| [Stephens et al. 2008](https://doi.org/10.1371/journal.pcbi.1000028) | P2 | verify | paper_audited; reusable_basis_asset_not_ingested | verify | unknown |
diff --git a/docs/DATA_CARD.md b/docs/DATA_CARD.md
new file mode 100644
index 0000000000000000000000000000000000000000..b712f0be48cf5389f0ce44c65693a08ea62e6f82
--- /dev/null
+++ b/docs/DATA_CARD.md
@@ -0,0 +1,99 @@
+
+# WormTrack-Verify — v0.1.0-alpha.1
+
+A public **development pilot** for testing anatomical verification of anonymous neuron trajectories in *C. elegans*. This is usable data and executable evaluation machinery. **The full initial benchmark and release gates A/B/C are not complete.** There is no frozen held-out test set in this release.
+
+## Motivation and scientific hypothesis
+
+Tracking errors contaminate neural activity measurements. Eutely supports persistent neuronal populations in adult hermaphrodites; it does not imply 302 visible detections or immutable positions. The hypothesis is that an independently estimated, restricted deformation model can predict neuronal positions with uncertainty sufficiently smaller than local inter-neuron spacing to expose identity errors. Four planar eigenworm modes do not establish a four-dimensional model of 3D internal tissue motion. The benchmark must allow this hypothesis to fail.
+
+## Inventory and actual included material
+
+| Component | Observed count / status |
+|---|---|
+| Recording-level registry | 264 |
+| Original WormID source assets | 118 |
+| ASCENT human-corrected observations | 105599 |
+| ASCENT anonymous tracks | 96 |
+| Hosted imaging excerpts | 44 volumes across four recordings |
+| Reference point clouds | 2 |
+| Independent behavior summaries | 6,400 samples; one SF acquisition |
+| Measured centerlines / eigenworm coefficients | 0; unavailable |
+| Frozen train/validation/test | Not released; development only |
+
+The five standardized tracking tables cover ASCENT, ZephIR, a NeRVE-derived recording redistributed in ZephIR, Targettrack, and a 16-frame WormID EY excerpt. Only ASCENT is currently eligible for the trusted tracking and corruption pilot. ZephIR scorer codes and Targettrack mask provenance are retained; unresolved annotations are **unknown**, not ground truth. The archive also contains a mouse experiment, which is excluded.
+
+All 36 WormID tracking acquisitions and 38 excluded SF recordings are mapped at inventory level. NP/HL structural recordings are not presented as dynamic tracking. [Assembly report](docs/ASSEMBLY_REPORT.md) distinguishes inventoried assets, inspected data, validated tables, and trusted correspondence labels. [Source audit](docs/SOURCE_AUDIT.md) records blocked resources.
+
+## Tracking versus verification
+
+`tracking_gt` isolates association on fixed source-supported objects and preserves the original WormID reference-frame protocol for future reproduction. `anatomical_verification` tests labeled identity corruptions. `anatomical_consistency` contains consistency diagnostics with incomplete labels. These protocols are distinct from train/validation/test partitions.
+
+The pilot provides two untrained oracle association baselines (Euclidean nearest neighbor and Hungarian assignment), six controlled error types, clean controls, and a fixed-reference-distance consistency baseline. Its threshold is a declared **5 micrometers**, chosen as a development demonstration, not calibrated to a guaranteed false-acceptance target. Reported results are descriptive development diagnostics. No full-image tracking, HOTA/IDF1, independent deformation fitting, or cross-laboratory generalization claim is made.
+
+## Data formats and coordinates
+
+Structured data are recording-partitioned Parquet. Original annotation HDF5 files and a small original structural NWB are retained where allowed. Imaging pilots use lossless chunked HDF5 with one CZYX dataset per hosted frame; source intensities are unnormalized. The logical volume axis order is TCZYX. This HDF5 representation is not advertised as OME-Zarr/NGFF.
+
+Point coordinates are named XYZ. Native voxel coordinates and transforms are preserved. Physical fields are null when calibration is unavailable. ASCENT spacing is (0.243, 0.243, 1.5) micrometers in XYZ, from the source record. WormID spacing comes from the inspected NWB. ZephIR normalized coordinates are converted through the source pixel-center convention; 17 out-of-bounds observations remain present with `validity=false`. Targettrack centroids remain in its source-processed image coordinates with an unresolved transform direction.
+
+## Ground-truth provenance
+
+ASCENT tracks were initialized with ZephIR and manually proofread/corrected by the source authors. This is **human-corrected algorithm output**, not independent de novo dense annotation. Source predictions live separately under `evaluation/source_predictions/` because their detections differ from oracle objects. Anonymous track IDs are sufficient; neuronal names are optional and are not used by the verifier.
+
+The WormID EY inspected source has 962 image frames and 961 segmentation frames. Only the available observations are published. No coordinate clamping, identity interpolation, or missing annotation fabrication is performed.
+
+## Anatomy and posture
+
+ASCENT's proofread first frame and a same-animal NeuroPAL structural cloud are included. Neither is a universal anatomical atlas. Unknown reference uncertainty is null. SF measured behavior summaries retain source timestamps and empty/unknown units. They are not centerlines, body coordinates, or a validated 3D deformation map. The client raises an explicit missing-component error for unsupported posture or imaging frames.
+
+## Splits and duplicates
+
+All included examples are assigned to `development`; train/validation/test are empty and no test performance is claimed. Original WormID fold values are preserved verbatim as a reproduction reference, not interpreted as this benchmark's partitions. The original split CSV has 120 rows and 118 unique filenames; two repeated structural entries are preserved in that reference table.
+
+Shared DANDI subject identifiers, particularly SK1 and DANDI 001623, are flagged as possible overlaps. Common animals/acquisitions and derivatives must stay together; unresolved overlap cannot be used to establish split disjointness. The NeRVE excerpt in ZephIR is not counted as an additional independent source acquisition. Test labels, when frozen in a later release, cannot calibrate corruptions or verification thresholds.
+
+## Access examples
+
+```python
+from datasets import load_dataset
+# Pin a commit SHA or the alpha tag after publication.
+registry = load_dataset("pytc/trackingBench", "recordings", revision="v0.1.0-alpha.1")
+tracks = load_dataset("pytc/trackingBench", "tracks", revision="v0.1.0-alpha.1", streaming=True)
+corruptions = load_dataset("pytc/trackingBench", "perturbations", revision="v0.1.0-alpha.1", streaming=True)
+print(next(iter(tracks["development"])))
+```
+
+The named configurations are `recordings`, `detections`, `tracks`, `posture`, `anatomy`, `perturbations`, and `verification`. The `posture` configuration currently contains **behavior summaries**, with explicit `component_type`, and zero measured centerlines. Loading tables does not download microscopy volumes. The recording registry includes inventory-only acquisitions; `hosted_tracks` and `hosted_volume` distinguish available components.
+
+For the bundled selective Python client, install this repository's small code subset:
+
+```python
+from huggingface_hub import snapshot_download
+import subprocess, sys
+code = snapshot_download("pytc/trackingBench", repo_type="dataset", revision="v0.1.0-alpha.1",
+                         allow_patterns=["src/**", "pyproject.toml", "README.md", "requirements.lock.txt"])
+subprocess.check_call([sys.executable, "-m", "pip", "install", code])
+from cell_tracking.data.wormtrack_verify import BenchmarkClient
+client = BenchmarkClient(revision="v0.1.0-alpha.1")
+annotations = client.load_tracks("ascent-opterra")
+volume = client.load_volume("ascent-opterra", frame=0)  # Small hosted excerpt only; CZYX.
+```
+
+Use [the executable pilot](examples/pilot.py) to reproduce the corruption and metric demonstration. Files larger than the client's 128 MiB budget require an explicit increased budget; no function silently downloads the benchmark or a multi-GB original movie. Original assets resolve through immutable DANDI asset IDs and versioned Zenodo records in `metadata/upstream_assets.json`. External assets are not Hub-hosted movies.
+
+## Licensing and attribution
+
+The original empty repository displayed MIT. That does not license third-party data. Included DANDI and Zenodo data are CC BY 4.0 per their release metadata. Benchmark helper code is MIT; source data retain their own licenses and attribution. Resources with unresolved redistribution rights remain references only. See [per-source licensing](docs/LICENSING.md), [catalog](docs/DATASET_CATALOG.md), and [citations](docs/CITATIONS.md).
+
+## Limitations and biases
+
+This pilot has only one trusted proofread tracking specimen, no measured centerlines, no independently calibrated deformation uncertainty, and no held-out evaluation. Unknown scorer codes are not promoted to manual ground truth. Fixed-reference residuals include legitimate tissue motion and are permutation-sensitive only because reference correspondences remain fixed. A static unordered point cloud cannot detect identity swaps. Acceptance by a geometric heuristic is not proof of biological identity.
+
+Coverage and selective risk use the full declared observation population; rejecting everything yields zero coverage. Sources differ in optics, visibility, annotation construction, and acquisition restrictions. The 302-neuron assumption is not universal across sex/stage and is never used to force observed counts.
+
+## Release history
+
+- `v0.1.0-alpha.1`: development pilot; source inventory, five standardized tracking tables, imaging excerpts, two reference clouds, measured behavior summaries, controlled corruptions, and reference baseline outputs. Full initial release gates remain open.
+
+Each release has a SHA256 assembly manifest, an upstream asset manifest, dependency pins, and machine-readable validation results. No model weights or externally pretrained models are used by these pilot baselines.
diff --git a/docs/LICENSING.md b/docs/LICENSING.md
new file mode 100644
index 0000000000000000000000000000000000000000..9758ea57db1053ab1e1538dc2bb7cb66077b9742
--- /dev/null
+++ b/docs/LICENSING.md
@@ -0,0 +1,9 @@
+# Per-source redistribution audit
+
+Included DANDI releases list `spdx:CC-BY-4.0`; included Zenodo records 17561700, 10818810, and 10008744 list `cc-by-4.0`. Original source versions, filenames, checksums, and citations accompany derived tables and excerpts. Conversion, axis permutations, centroid extraction, and controlled identity errors are benchmark modifications, not original source annotations.
+
+NeRVE-derived annotations are obtained from the CC-BY-4.0 ZephIR release and identified as the original Nguyen et al. recording. This does not establish the license of every original IEEE-hosted NeRVE asset.
+
+CeNDeR's GitHub MIT license covers its code; the OSF data project has no stated data license. CeNDeR imagery/annotations are not redistributed. The Skuhersky, NeuroPAL canonical-position, and statistical-atlas repositories have no independently verified data license in this audit; they are linked only. BrainAlignNet and OpenWorm access/licensing remain unresolved. Eigenworm paper access does not automatically establish licensing for an arbitrary downloadable basis.
+
+Benchmark helper code is MIT, provided in `LICENSE-code.txt`. Data are not uniformly MIT. CC BY 4.0: https://creativecommons.org/licenses/by/4.0/ . The assembly excludes assets with unresolved data redistribution permissions.
diff --git a/docs/SCHEMAS.md b/docs/SCHEMAS.md
new file mode 100644
index 0000000000000000000000000000000000000000..ec105f990ac3fe13ae1a02ff0e6830b7635059ec
--- /dev/null
+++ b/docs/SCHEMAS.md
@@ -0,0 +1,13 @@
+# Schema and coordinate version 0.1.0-alpha.1
+
+Recording registry: one row per source asset/recording, not a guaranteed unique physical animal. Stable `recording_id`, `source_dataset`, source version/asset/path/checksum, lab/animal/session IDs, license, QC and hosted-component flags are required. Nullable biological fields stay unknown until directly inspected. JSON strings encode optional TCZYX shapes, XYZ voxel spacings, channel arrays, and source digest mappings.
+
+Tracking: one row per observation; scope is recording + label_set + frame_index. Detection IDs and track assignments are unique in each valid scope. Native XYZ voxel fields remain present. Physical XYZ micrometers are nullable. `annotation_type`, raw scorer provenance, validity, and coordinate system distinguish trusted annotations, predictions, and unresolved provenance. Missing source observations are not interpolated; dropped perturbation observations remain rows with validity false.
+
+ZephIR: normalized point coordinates map to continuous voxel centers as `normalized * axis_size - 0.5`, inverse of the native GUI's `(index + 0.5)/shape`. Original normalized values remain present. Out-of-bounds rows are preserved and flagged. Native imagery and annotations remain inspectable.
+
+Targettrack: original frame CXYZ is permuted to CZYX. Mask centers are computed in native XYZ, separately from the unresolved original-to-processed affine direction. This is derived localization from a mask, with unknown annotation quality. No micron spacing is guessed.
+
+Anatomy: same-animal reference origin is explicit; uncertainty is null where unknown. Population atlases are not substituted for temporal truth. SF behavior summaries preserve original timestamps, signal paths and empty source units; they cannot be interpreted as centerlines or synchronized 3D body transport without further audit.
+
+Schemas are in `schemas/`, full Parquet types in `schemas/arrow_schemas.json`, and integrity results in `evaluation/validation.json`. Large structured tables are recording-partitioned. The standard library client never bulk downloads volumes.
diff --git a/docs/SOURCE_AUDIT.md b/docs/SOURCE_AUDIT.md
new file mode 100644
index 0000000000000000000000000000000000000000..a388c2cae6ae36cbff6a04f2101dc6e8b83b658b
--- /dev/null
+++ b/docs/SOURCE_AUDIT.md
@@ -0,0 +1,11 @@
+# Source audit
+
+The audit pins nine published DANDI versions, inventories 260 immutable NWB assets, and maps all 118 original WormID assets plus the exact 36 tracking acquisitions (EY 21, KK 9, SK1 4, SK2 2). SF's 38 records remain weakly labeled, and NP/HL remain structural. The original split file has two repeated structural rows. Raw image shape is checked on acquired or range-read pilot assets, not inferred solely from publications.
+
+The ZephIR ZIP central directory was range-read before acquisition. Only small worm annotation files and one 125 MB compressed image member were acquired; the mouse recording and all macOS metadata are excluded. Per-member extraction checks ZIP CRC, while the full 14.9 GB archive MD5 is only an upstream reference and is **not verified locally**. Targettrack's 164 MB volumetric archive and ASCENT CSV files pass source MD5 checks. Derived/selected image assets have their own SHA256 checksums; selective NWB/HDF5 reads do not verify the full source file checksum.
+
+Shared DANDI subject identifiers produce potential overlap groups. DANDI 001623 overlaps identifiers from SK1, so independent specimen counts cannot be inferred by summing registry rows. NeRVE-in-ZephIR is a derivative of an existing acquisition. Skuhersky and NeuroPAL canonical CSVs appear identical in published Git trees; they are references only and not counted as two anatomical datasets.
+
+Known blocks: CeNDeR OSF native storage lists no files, linked Google Drive API returns 404, and OSF data license is unstated. BrainAlignNet Dataverse API did not return usable metadata. OpenWorm movement hostname fails DNS. NeRVE original IEEE page resolves but its full recording inventory and download terms are not completed. Reusable population atlas licensing and an eigenworm basis remain unresolved. None of these blocked sources is silently replaced with synthetic data.
+
+A total source storage inventory is an upper bound that includes duplicates. The original SF corpus alone exceeds 1 TB. This alpha release selectively acquires small source files and range reads and publishes a compact subset (exact hosted bytes in the assembly manifest; total HTTP transfer bytes were not measured), avoiding duplicated multi-terabyte movies. Extended full acquisition and hosting require a deduplicated transfer budget. No scheduler jobs or immutable experiment worktrees are modified.
diff --git a/evaluation/baseline_predictions/hungarian.parquet b/evaluation/baseline_predictions/hungarian.parquet
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+version https://git-lfs.github.com/spec/v1
+oid sha256:4debb7bd0219c01457c53556c27144f60cb5a038eb1546b8524df46cd4f7152e
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diff --git a/evaluation/baseline_predictions/nearest_neighbor.parquet b/evaluation/baseline_predictions/nearest_neighbor.parquet
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+version https://git-lfs.github.com/spec/v1
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diff --git a/evaluation/loading_validation.json b/evaluation/loading_validation.json
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+++ b/evaluation/loading_validation.json
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+{
+  "status": "passed",
+  "local_dataset_configs": [
+    {
+      "config": "recordings",
+      "streaming": false,
+      "status": "passed",
+      "rows": 264
+    },
+    {
+      "config": "recordings",
+      "streaming": true,
+      "status": "passed",
+      "rows": null
+    },
+    {
+      "config": "detections",
+      "streaming": false,
+      "status": "passed",
+      "rows": 420470
+    },
+    {
+      "config": "detections",
+      "streaming": true,
+      "status": "passed",
+      "rows": null
+    },
+    {
+      "config": "tracks",
+      "streaming": false,
+      "status": "passed",
+      "rows": 420470
+    },
+    {
+      "config": "tracks",
+      "streaming": true,
+      "status": "passed",
+      "rows": null
+    },
+    {
+      "config": "posture",
+      "streaming": false,
+      "status": "passed",
+      "rows": 6400
+    },
+    {
+      "config": "posture",
+      "streaming": true,
+      "status": "passed",
+      "rows": null
+    },
+    {
+      "config": "anatomy",
+      "streaming": false,
+      "status": "passed",
+      "rows": 292
+    },
+    {
+      "config": "anatomy",
+      "streaming": true,
+      "status": "passed",
+      "rows": null
+    },
+    {
+      "config": "perturbations",
+      "streaming": false,
+      "status": "passed",
+      "rows": 739193
+    },
+    {
+      "config": "perturbations",
+      "streaming": true,
+      "status": "passed",
+      "rows": null
+    },
+    {
+      "config": "verification",
+      "streaming": false,
+      "status": "passed",
+      "rows": 739193
+    },
+    {
+      "config": "verification",
+      "streaming": true,
+      "status": "passed",
+      "rows": null
+    }
+  ],
+  "hosted_volumes_validated": 44,
+  "test_split_status": "absent; development_only",
+  "independent_population_atlas": "not_included",
+  "centerline_status": "unavailable",
+  "fresh_environment_pilot": "passed; isolated environment, installed bundled package, loaded actual ASCENT tracks/reference/image, generated corruption and computed metrics",
+  "contract_tests": "21 passed",
+  "lint": "Ruff passed for changed Python",
+  "wormid_exact_tracking_subset": 36,
+  "sf_inventory": 38,
+  "source_behavior_timestamps": "monotonic for all four included signals"
+}
diff --git a/evaluation/pilot_results.json b/evaluation/pilot_results.json
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+{
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diff --git a/evaluation/remote_validation.json b/evaluation/remote_validation.json
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+{
+  "status": "passed",
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+      "status": "passed"
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+      "status": "passed"
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+      "status": "passed"
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+      "rows": 420470,
+      "status": "passed"
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+      "config": "tracks",
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+      "status": "passed"
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+    {
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+      "rows": 6400,
+      "status": "passed"
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+    {
+      "config": "posture",
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+      "rows": null,
+      "status": "passed"
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+    {
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+      "streaming": false,
+      "rows": 292,
+      "status": "passed"
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+    {
+      "config": "anatomy",
+      "streaming": true,
+      "rows": null,
+      "status": "passed"
+    },
+    {
+      "config": "perturbations",
+      "streaming": false,
+      "rows": 739193,
+      "status": "passed"
+    },
+    {
+      "config": "perturbations",
+      "streaming": true,
+      "rows": null,
+      "status": "passed"
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+      "rows": 739193,
+      "status": "passed"
+    },
+    {
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+      "streaming": true,
+      "rows": null,
+      "status": "passed"
+    }
+  ],
+  "fresh_environment": true,
+  "remote_snapshot_sha256_files": 82,
+  "source_workspace_required": false
+}
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diff --git a/evaluation/risk_coverage/nearby_swap:seed20261007.parquet b/evaluation/risk_coverage/nearby_swap:seed20261007.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..d0a34de565aebcbf848db7939f37358b97663c64
--- /dev/null
+++ b/evaluation/risk_coverage/nearby_swap:seed20261007.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ee368e1925ed5be3bd855df830a9861b79a3faef66b55dd348ac7f7842bfbe1
+size 275412
diff --git a/evaluation/risk_coverage/persistent_swap:seed20261007.parquet b/evaluation/risk_coverage/persistent_swap:seed20261007.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..2cf8e9d0fd69a2b7fec011204ab9341395e6f797
--- /dev/null
+++ b/evaluation/risk_coverage/persistent_swap:seed20261007.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32f7e9b1db8e4abcebfe26ba31c5493da628760840b8cb749a5fff62e5dbc220
+size 239875
diff --git a/evaluation/risk_coverage/single_frame_swap:seed20261007.parquet b/evaluation/risk_coverage/single_frame_swap:seed20261007.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..86466e389dad923718c8807dedcbe19dc1087394
--- /dev/null
+++ b/evaluation/risk_coverage/single_frame_swap:seed20261007.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/evaluation/risk_coverage/swap_recovery:seed20261007.parquet b/evaluation/risk_coverage/swap_recovery:seed20261007.parquet
new file mode 100644
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--- /dev/null
+++ b/evaluation/risk_coverage/swap_recovery:seed20261007.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/evaluation/source_predictions/ascent.parquet b/evaluation/source_predictions/ascent.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..08ba53d3591f8bfd4274308b19de885e15dc31ea
--- /dev/null
+++ b/evaluation/source_predictions/ascent.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 1143586
diff --git a/evaluation/validation.json b/evaluation/validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..fc8eaf43185e18d864b59bf2b2d38527121dda75
--- /dev/null
+++ b/evaluation/validation.json
@@ -0,0 +1,60 @@
+{
+  "status": "passed",
+  "scope": "published_table_invariants",
+  "tables": {
+    "ascent-opterra": {
+      "observations": 105599,
+      "frames": 1100,
+      "tracks": 96,
+      "annotation_types": {
+        "human_corrected": 105599
+      },
+      "invalid_observations_preserved": 0
+    },
+    "ascent-source-predictions": {
+      "observations": 105599,
+      "frames": 1100,
+      "tracks": 98,
+      "annotation_types": {
+        "algorithm_prediction": 105599
+      }
+    },
+    "zephir-zm9624": {
+      "observations": 188663,
+      "frames": 1060,
+      "tracks": 178,
+      "annotation_types": {
+        "unknown": 188663
+      },
+      "invalid_observations_preserved": 17
+    },
+    "nerve-al-w1-via-zephir": {
+      "observations": 121344,
+      "frames": 1536,
+      "tracks": 79,
+      "annotation_types": {
+        "unknown": 119019,
+        "algorithm_prediction": 2325
+      },
+      "invalid_observations_preserved": 0
+    },
+    "targettrack-epfl10": {
+      "observations": 1951,
+      "frames": 147,
+      "tracks": 15,
+      "annotation_types": {
+        "unknown": 1951
+      },
+      "invalid_observations_preserved": 0
+    },
+    "dandi-000541-sub-20190924-01": {
+      "observations": 2896,
+      "frames": 16,
+      "tracks": 181,
+      "annotation_types": {
+        "unknown": 2896
+      },
+      "invalid_observations_preserved": 0
+    }
+  }
+}
diff --git a/examples/pilot.py b/examples/pilot.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7eb70847ee25799b89770f354890a6537804499
--- /dev/null
+++ b/examples/pilot.py
@@ -0,0 +1,44 @@
+"""Run the actual ASCENT pilot; accept either a local assembly or pinned Hub revision."""
+
+from __future__ import annotations
+
+import argparse
+import json
+
+from cell_tracking.data.wormtrack_verify import BenchmarkClient
+from cell_tracking.evaluation.wormtrack_verify import (
+    association_metrics,
+    corrupt_tracks,
+    reference_residual_scores,
+    verifier_metrics,
+)
+
+
+def run(client):
+    clean = client.load_tracks("ascent-opterra", label_source="human_corrected")
+    cloud = client.load_reference_cloud("ascent-opterra")
+    corrupt = corrupt_tracks(
+        clean, kind="swap_recovery", seed=20261007, start_frame=300, stop_frame=700
+    )
+    scores = reference_residual_scores(corrupt, cloud)
+    metrics = verifier_metrics(scores, corrupt.is_error, threshold=5.0)
+    metrics.pop("risk_coverage")
+    volume = client.load_volume("ascent-opterra", frame=0)
+    print(
+        json.dumps(
+            {
+                "volume_czyx": list(volume.shape),
+                "association": association_metrics(clean, corrupt),
+                "verifier": metrics,
+            },
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--root")
+    parser.add_argument("--revision")
+    args = parser.parse_args()
+    run(BenchmarkClient(root=args.root, revision=args.revision))
diff --git a/examples/stream.py b/examples/stream.py
new file mode 100644
index 0000000000000000000000000000000000000000..6daa7211e7dd9688aeb40fcf2d32d69983ff625d
--- /dev/null
+++ b/examples/stream.py
@@ -0,0 +1,12 @@
+"""Inspect the published annotation stream without downloading microscopy volumes."""
+
+from datasets import load_dataset
+
+stream = load_dataset(
+    "pytc/trackingBench",
+    name="tracks",
+    revision="v0.1.0-alpha.1",
+    split="development",
+    streaming=True,
+)
+print(next(iter(stream)))
diff --git a/metadata/assembly_counts.json b/metadata/assembly_counts.json
new file mode 100644
index 0000000000000000000000000000000000000000..813275a38d2a06feae798bcac32016d66508a60a
--- /dev/null
+++ b/metadata/assembly_counts.json
@@ -0,0 +1,64 @@
+{
+  "version": "0.1.0-alpha.1",
+  "assembled_at": "2026-10-07T21:32:05.000162-04:00",
+  "inventory_recordings": 264,
+  "wormid_inventory_assets": 118,
+  "ingested": {
+    "ascent-opterra": {
+      "observations": 105599,
+      "frames": 1100,
+      "tracks": 96,
+      "annotation_types": {
+        "human_corrected": 105599
+      },
+      "invalid_observations_preserved": 0
+    },
+    "zephir-zm9624": {
+      "observations": 188663,
+      "frames": 1060,
+      "tracks": 178,
+      "annotation_types": {
+        "unknown": 188663
+      },
+      "invalid_observations_preserved": 17
+    },
+    "nerve-al-w1-via-zephir": {
+      "observations": 121344,
+      "frames": 1536,
+      "tracks": 79,
+      "annotation_types": {
+        "unknown": 119019,
+        "algorithm_prediction": 2325
+      },
+      "invalid_observations_preserved": 0
+    },
+    "targettrack-epfl10": {
+      "observations": 1951,
+      "frames": 147,
+      "tracks": 15,
+      "annotation_types": {
+        "unknown": 1951
+      },
+      "invalid_observations_preserved": 0
+    },
+    "dandi-000541-sub-20190924-01": {
+      "observations": 2896,
+      "frames": 16,
+      "tracks": 181,
+      "annotation_types": {
+        "unknown": 2896
+      },
+      "invalid_observations_preserved": 0
+    }
+  },
+  "source_inventory_entries": 20,
+  "pilot_imaging_frames": 44,
+  "anatomical_clouds": 2,
+  "measured_centerlines": 0,
+  "independent_behavior_summary_recordings": 1,
+  "release_gates": {
+    "A": "partial: full non-DANDI inventories pending",
+    "B": "partial: independent moving-worm GT and centerline pending",
+    "C": "not_passed: all P0 ingestion and held-out evaluation pending"
+  }
+}
diff --git a/metadata/assembly_manifest.json b/metadata/assembly_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..8ae4f72e34301243eaefabfaebbf640e07f093cf
--- /dev/null
+++ b/metadata/assembly_manifest.json
@@ -0,0 +1,426 @@
+{
+  "version": "0.1.0-alpha.1",
+  "upstream_manifest": "metadata/upstream_assets.json",
+  "files": [
+    {
+      "path": "LICENSE-code.txt",
+      "bytes": 1086,
+      "sha256": "688b6af765b57ae40f19fe411ae0c6f1a281358de28bba1602740487fd2f761e"
+    },
+    {
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+      "sha256": "3d49b9e328808cb7de2225831865cdefd13d7477033287b73a4104d96dac982e"
+    },
+    {
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+      "bytes": 112,
+      "sha256": "f92ba680ded107b26036468fc2e24a043db9959888623635fc6983af537d8a3c"
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+      },
+      {
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+      },
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diff --git a/metadata/sf_behavior_provenance.json b/metadata/sf_behavior_provenance.json
new file mode 100644
index 0000000000000000000000000000000000000000..525d3c8f0ec21713925cc85b02abeabb715b9291
--- /dev/null
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+{
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diff --git a/metadata/wormid_ey_image_annotation_discrepancy.json b/metadata/wormid_ey_image_annotation_discrepancy.json
new file mode 100644
index 0000000000000000000000000000000000000000..c57382e760b364795d84090da165eeee1879dc89
--- /dev/null
+++ b/metadata/wormid_ey_image_annotation_discrepancy.json
@@ -0,0 +1,28 @@
+{
+  "asset": {
+    "asset_id": "bd4debcc-4734-4824-affa-15a914fa4d4b",
+    "blob": "a686daf2-28d3-452c-9375-54d91fe4994d",
+    "zarr": null,
+    "path": "sub-20190924-01/sub-20190924-01_ses-20190924_ophys.nwb",
+    "size": 1455547324,
+    "created": "2024-05-03T19:37:16.142149Z",
+    "modified": "2024-06-11T19:47:10.942043Z"
+  },
+  "version": "0.241009.1457",
+  "shape_tczyx": [
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+    1,
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+    128,
+    256
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+  "spacing_xyz": [
+    0.266666666666667,
+    0.266666666666667,
+    1.5
+  ],
+  "spacing_unit": "micrometers",
+  "starting_time": 0.0,
+  "rate_hz": 4.0,
+  "original_segmentations": 961
+}
diff --git a/perturbations/README.md b/perturbations/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a3c94e345626e32ad26ad188c4e1c44b16ece3f7
--- /dev/null
+++ b/perturbations/README.md
@@ -0,0 +1 @@
+Materialized pilot scenarios live in data/perturbations; exact row-level labels live in data/verification. See docs/BENCHMARK_SPEC.md.
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..637b35e51964eba8a393b8d175e4c86d40f75e03
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,13 @@
+[build-system]
+requires = ["hatchling>=1.25"]
+build-backend = "hatchling.build"
+[project]
+name = "wormtrack-verify"
+version = "0.1.0a1"
+requires-python = ">=3.12"
+description = "Selective client and evaluation helpers for WormTrack-Verify"
+readme = "README.md"
+license = {text = "MIT (helper code only)"}
+dependencies = ["huggingface_hub>=1.0", "pyarrow>=20", "pandas>=2.2", "h5py>=3.12", "numpy>=2.2", "scipy>=1.15", "scikit-learn>=1.6", "requests>=2.32", "PyYAML>=6"]
+[tool.hatch.build.targets.wheel]
+packages = ["src/cell_tracking"]
diff --git a/requirements.lock.txt b/requirements.lock.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bf93876e52b69b009e54154cf6e02ff5dc4d89bd
--- /dev/null
+++ b/requirements.lock.txt
@@ -0,0 +1,116 @@
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+PyYAML==6.0.3
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+Pygments==2.21.0
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+frozenlist==1.8.0
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+fsspec==2026.7.0
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+httpx2==2.13.1
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+multidict==6.9.1
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+pandas==3.0.6
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+pluggy==1.6.0
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+propcache==0.5.4
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+python-dateutil==2.9.0.post0
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+referencing==0.37.0
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+requests==2.34.2
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+rpds-py==2026.9.1
+ruff==0.16.10
+ruff==0.16.10
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+scikit-learn==1.9.1
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+scipy==1.18.1
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+six==1.17.0
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+tifffile==2026.9.20
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+tqdm==4.70.1
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+truststore==0.10.4
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+typing_extensions==4.16.0
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+urllib3==2.8.0
+xxhash==4.0.1
+xxhash==4.0.1
+yarl==1.25.1
+yarl==1.25.1
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+zarr==3.4.0
diff --git a/schemas/arrow_schemas.json b/schemas/arrow_schemas.json
new file mode 100644
index 0000000000000000000000000000000000000000..651e1bbfd027a87d13194e1623378ca19a8a7587
--- /dev/null
+++ b/schemas/arrow_schemas.json
@@ -0,0 +1,1554 @@
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+      "name": "wormid_fold",
+      "type": "large_string",
+      "nullable": true
+    }
+  ]
+}
diff --git a/schemas/assembly_manifest.schema.json b/schemas/assembly_manifest.schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..ccf9d311f547eb8a39c456e9955f8c5344b37cbb
--- /dev/null
+++ b/schemas/assembly_manifest.schema.json
@@ -0,0 +1,44 @@
+{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "title": "WormTrackVerifyAssemblyManifest",
+  "type": "object",
+  "required": [
+    "version",
+    "files",
+    "upstream_manifest"
+  ],
+  "additionalProperties": false,
+  "properties": {
+    "version": {
+      "type": "string"
+    },
+    "upstream_manifest": {
+      "const": "metadata/upstream_assets.json"
+    },
+    "files": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "required": [
+          "path",
+          "bytes",
+          "sha256"
+        ],
+        "additionalProperties": false,
+        "properties": {
+          "path": {
+            "type": "string"
+          },
+          "bytes": {
+            "type": "integer",
+            "minimum": 0
+          },
+          "sha256": {
+            "type": "string",
+            "pattern": "^[0-9a-f]{64}$"
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/schemas/track_observation.schema.json b/schemas/track_observation.schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..0953956d1d82d50890176305c1a3c8fe177708bd
--- /dev/null
+++ b/schemas/track_observation.schema.json
@@ -0,0 +1,70 @@
+{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "type": "object",
+  "required": [
+    "recording_id",
+    "frame_index",
+    "detection_id",
+    "track_id",
+    "x_voxel",
+    "y_voxel",
+    "z_voxel",
+    "annotation_type",
+    "validity"
+  ],
+  "properties": {
+    "recording_id": {
+      "type": "string"
+    },
+    "frame_index": {
+      "type": "integer",
+      "minimum": 0
+    },
+    "detection_id": {
+      "type": "string"
+    },
+    "track_id": {
+      "type": "string"
+    },
+    "x_voxel": {
+      "type": "number"
+    },
+    "y_voxel": {
+      "type": "number"
+    },
+    "z_voxel": {
+      "type": "number"
+    },
+    "x_um": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "y_um": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "z_um": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "annotation_type": {
+      "enum": [
+        "human_corrected",
+        "manually_verified",
+        "verified_checkpoint",
+        "algorithm_prediction",
+        "unknown"
+      ]
+    },
+    "validity": {
+      "type": "boolean"
+    }
+  },
+  "additionalProperties": true
+}
diff --git a/splits/assignments.json b/splits/assignments.json
new file mode 100644
index 0000000000000000000000000000000000000000..1fd04c5b3d794510d8591e974ddccda6c338c4bf
--- /dev/null
+++ b/splits/assignments.json
@@ -0,0 +1,40 @@
+{
+  "seed": 20261007,
+  "status": "development_only_no_frozen_test",
+  "protocols": {
+    "tracking_gt": {
+      "train": [],
+      "validation": [],
+      "test": [],
+      "development": [
+        "ascent-opterra"
+      ],
+      "quarantine": []
+    },
+    "anatomical_verification": {
+      "train": [],
+      "validation": [],
+      "test": [],
+      "development": [
+        "ascent-opterra"
+      ],
+      "quarantine": []
+    },
+    "anatomical_consistency": {
+      "train": [],
+      "validation": [],
+      "test": [],
+      "development": [
+        "ascent-opterra",
+        "dandi-000541-sub-20190924-01",
+        "dandi-000715-sub-56-YAaDV",
+        "dandi-000776-sub-2022-06-14-13-SWF702",
+        "nerve-al-w1-via-zephir",
+        "targettrack-epfl10",
+        "zephir-zm9624"
+      ],
+      "quarantine": []
+    }
+  },
+  "original_wormid_commit": "0c74e9ddffee138cfc6330ab65c0e690e8e199dd"
+}
diff --git a/splits/original_wormid_folds.parquet b/splits/original_wormid_folds.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..d45144df38d5c05cd69e7aa9d87c822ea01df623
--- /dev/null
+++ b/splits/original_wormid_folds.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17c84659e3853c6ce3b0adb87c04fe7970ff667cf91b2c3f8854fbd1b071361d
+size 12951
diff --git a/src/cell_tracking/__init__.py b/src/cell_tracking/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5eb8a428df5ef22e0fe99633570b2be77ae84d3
--- /dev/null
+++ b/src/cell_tracking/__init__.py
@@ -0,0 +1 @@
+"""WormTrack-Verify benchmark helpers."""
diff --git a/src/cell_tracking/data/__init__.py b/src/cell_tracking/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5eb8a428df5ef22e0fe99633570b2be77ae84d3
--- /dev/null
+++ b/src/cell_tracking/data/__init__.py
@@ -0,0 +1 @@
+"""WormTrack-Verify benchmark helpers."""
diff --git a/src/cell_tracking/data/wormtrack_verify/__init__.py b/src/cell_tracking/data/wormtrack_verify/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a087ed85395f7e56af617a2c6296450ab08ba730
--- /dev/null
+++ b/src/cell_tracking/data/wormtrack_verify/__init__.py
@@ -0,0 +1,5 @@
+"""Selective access and assembly tools for the WormTrack-Verify pilot."""
+
+from .client import BenchmarkClient, MissingComponentError
+
+__all__ = ["BenchmarkClient", "MissingComponentError"]
diff --git a/src/cell_tracking/data/wormtrack_verify/adapters.py b/src/cell_tracking/data/wormtrack_verify/adapters.py
new file mode 100644
index 0000000000000000000000000000000000000000..6090b15a998a8b2c7399be284d802abc846ee33e
--- /dev/null
+++ b/src/cell_tracking/data/wormtrack_verify/adapters.py
@@ -0,0 +1,126 @@
+"""Native ASCENT and ZephIR table adapters with conservative annotation provenance."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+XYZ = ["x_voxel", "y_voxel", "z_voxel"]
+TRUSTED = {"human_corrected", "manually_verified", "verified_checkpoint"}
+
+
+def _physical(df, spacing_xyz):
+    for axis, scale in zip("xyz", spacing_xyz or [None] * 3, strict=True):
+        df[f"{axis}_um"] = df[f"{axis}_voxel"] * scale if scale is not None else np.nan
+    df["coordinate_system"] = "source_microscope"
+    df["annotation_confidence"] = np.nan
+    df["validity"] = True
+    return df
+
+
+def ascent_tracks(path, recording_id, *, predicted=False):
+    """Read native ZYX CSV; physical spacing is from Zenodo 17561700 metadata."""
+    df = pd.read_csv(path).rename(
+        columns={
+            "TrackID": "track_id",
+            "ObjectID": "detection_id",
+            "t": "frame_index",
+            "x": "x_voxel",
+            "y": "y_voxel",
+            "z": "z_voxel",
+        }
+    )
+    required = {"track_id", "detection_id", "frame_index", *XYZ}
+    if not required.issubset(df.columns):
+        raise ValueError(f"Missing ASCENT fields: {sorted(required - set(df.columns))}")
+    df["recording_id"] = recording_id
+    df["annotation_type"] = "algorithm_prediction" if predicted else "human_corrected"
+    df["source_provenance"] = "ASCENT-PRED" if predicted else "ASCENT-GT-proofread-ZephIR"
+    df["label_set"] = "prediction" if predicted else "reference"
+    df["track_id"] = df.track_id.astype(str)
+    df["detection_id"] = df.detection_id.map(lambda i: f"{'pred' if predicted else 'gt'}:{i}")
+    return _physical(df, [0.243, 0.243, 1.5])
+
+
+def zephir_tracks(path, metadata, recording_id):
+    """Retain scorer codes; only documented ZEIR results are classified as predictions.
+
+    Other codes remain unknown until their human/algorithm provenance is verified.
+    Normalized coordinates map to voxel index x*size-0.5 using the native viewer
+    convention. No voxel spacing is inferred from the neuron arrangement.
+    """
+    import h5py
+
+    with h5py.File(path, "r") as h:
+        df = pd.DataFrame({k: h[k][()] for k in h})
+    df = df.rename(
+        columns={
+            "t_idx": "frame_index",
+            "worldline_id": "track_id",
+            "id": "detection_id",
+            "provenance": "source_provenance",
+        }
+    )
+    df["source_provenance"] = df.source_provenance.map(lambda s: s.decode("utf-8"))
+    for axis in "xyz":
+        df[f"{axis}_normalized"] = df[axis]
+        df[f"{axis}_voxel"] = df[axis] * metadata[f"shape_{axis}"] - 0.5
+        df = df.drop(columns=axis)
+    df["annotation_type"] = np.where(
+        df.source_provenance == "ZEIR", "algorithm_prediction", "unknown"
+    )
+    df["recording_id"] = recording_id
+    df["label_set"] = "source_mixed"
+    df["track_id"] = df.track_id.astype(str)
+    df["detection_id"] = df.detection_id.map(lambda i: f"source:{i}")
+    df = _physical(df, None)
+    normalized = df[[f"{axis}_normalized" for axis in "xyz"]].to_numpy(dtype=float)
+    df["validity"] = (
+        np.isfinite(normalized).all(axis=1)
+        & (normalized >= 0).all(axis=1)
+        & (normalized <= 1).all(axis=1)
+    )
+    return df
+
+
+def validate_tracks(df, *, shape_xyz=None):
+    """Reject ambiguous assignments and nonfinite observed points; never clamp or interpolate."""
+    required = {
+        "recording_id",
+        "frame_index",
+        "track_id",
+        "detection_id",
+        *XYZ,
+        "annotation_type",
+        "label_set",
+        "validity",
+    }
+    if not required.issubset(df.columns):
+        raise ValueError(f"Missing fields: {sorted(required - set(df.columns))}")
+    if df.empty:
+        raise ValueError("Empty tracking table")
+    valid = df.loc[df.validity]
+    scope = ["recording_id", "label_set", "frame_index"]
+    if valid.duplicated(scope + ["track_id"]).any():
+        raise ValueError("Duplicate track assignments")
+    if valid.duplicated(scope + ["detection_id"]).any():
+        raise ValueError("Duplicate detection assignments")
+    if valid.track_id.isna().any() or valid.detection_id.isna().any():
+        raise ValueError("Missing identity on a valid observation")
+    if not np.isfinite(valid[XYZ].to_numpy(dtype=float)).all():
+        raise ValueError("Nonfinite observed coordinates")
+    if (valid.frame_index < 0).any() or (valid.frame_index % 1 != 0).any():
+        raise ValueError("Invalid frame index")
+    # Centers use pixel boundaries [-0.5, size-0.5], including ZephIR normalized endpoints.
+    if shape_xyz is not None:
+        points = valid[XYZ].to_numpy(dtype=float)
+        if ((points < -0.5) | (points > np.asarray(shape_xyz) - 0.5)).any():
+            raise ValueError("Spatial coordinates outside source bounds")
+    return {
+        "observations": len(valid),
+        "frames": int(valid.frame_index.nunique()),
+        "tracks": int(valid.track_id.nunique()),
+        "annotation_types": {
+            str(k): int(v) for k, v in valid.annotation_type.value_counts().items()
+        },
+    }
diff --git a/src/cell_tracking/data/wormtrack_verify/assembly.py b/src/cell_tracking/data/wormtrack_verify/assembly.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cf3f48ac492d3fc5868cfef12ad12bf6abda2f7
--- /dev/null
+++ b/src/cell_tracking/data/wormtrack_verify/assembly.py
@@ -0,0 +1,696 @@
+"""Build a development pilot from audited, checksum-pinned upstream assets.
+
+Usage: python -m cell_tracking.data.wormtrack_verify.assembly WORKSPACE OUTPUT
+Acquisition is deliberately separate: this command never downloads large files.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import shutil
+import sys
+from datetime import datetime
+from pathlib import Path
+from zoneinfo import ZoneInfo
+
+import h5py
+import numpy as np
+import pandas as pd
+
+from cell_tracking.data.wormtrack_verify.adapters import (
+    XYZ,
+    ascent_tracks,
+    validate_tracks,
+    zephir_tracks,
+)
+from cell_tracking.data.wormtrack_verify.inventory import DANDI_COHORTS, WORMND_COMMIT
+from cell_tracking.evaluation.wormtrack_verify import (
+    association_metrics,
+    corrupt_tracks,
+    oracle_association_baseline,
+    reference_residual_scores,
+    verifier_metrics,
+)
+
+VERSION = "0.1.0-alpha.1"
+SEED = 20261007
+
+
+def digest(path, algorithm="sha256"):
+    h = hashlib.new(algorithm)
+    with Path(path).open("rb") as stream:
+        for chunk in iter(lambda: stream.read(8 * 1024**2), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def dump(path, data):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(data, indent=2, allow_nan=False) + "\n")
+
+
+def table(root, path, df):
+    target = root / path
+    target.parent.mkdir(parents=True, exist_ok=True)
+    df.to_parquet(target, index=False)
+    return path
+
+
+def harmonize_tables(root):
+    """Align per-recording Parquet schemas without dropping source-specific fields."""
+    import pyarrow as pa
+    import pyarrow.parquet as pq
+
+    for folder in Path(root).glob("data/*"):
+        files = sorted(folder.glob("*.parquet"))
+        if not files:
+            continue
+        schema = pa.unify_schemas(
+            [pq.read_schema(path) for path in files], promote_options="permissive"
+        ).remove_metadata()
+        for path in files:
+            original = pq.read_table(path)
+            arrays = [
+                original[field.name].cast(field.type)
+                if field.name in original.column_names
+                else pa.nulls(original.num_rows, type=field.type)
+                for field in schema
+            ]
+            pq.write_table(pa.Table.from_arrays(arrays, schema=schema), path)
+
+
+def build(workspace, destination):
+    workspace, root = Path(workspace), Path(destination)
+    root.mkdir(parents=True, exist_ok=True)
+    audit, raw = workspace / "audit", workspace / "raw"
+    now = datetime.now(ZoneInfo("America/New_York")).isoformat()
+    sources, recordings, upstream, overlaps = [], [], [], []
+    original = pd.read_csv(audit / "wormid-tracking-split.csv")
+    split_lookup = original.drop_duplicates("filename").set_index("filename")
+    provenance_path = audit / "dandi-asset-provenance.json"
+    provenance = json.loads(provenance_path.read_text()) if provenance_path.exists() else {}
+    subject_groups = {}
+    for identifier, cohort in DANDI_COHORTS.items():
+        release = json.loads((audit / f"dandi-{identifier}-pinned.json").read_text())
+        assets = json.loads((audit / f"dandi-{identifier}-assets.json").read_text())
+        sources.append(
+            {
+                "source_id": f"dandi-{identifier}",
+                "name": cohort,
+                "priority": "P0" if identifier not in {"001623", "000981"} else "P1",
+                "version": assets["version"],
+                "url": f"https://dandiarchive.org/dandiset/{identifier}/{assets['version']}",
+                "license": release.get("license"),
+                "license_status": "verified_release_metadata",
+                "asset_count": len(assets["assets"]),
+                "bytes": sum(a["size"] for a in assets["assets"]),
+                "assembly_status": "inventoried_assets_not_bulk_ingested",
+            }
+        )
+        for asset in assets["assets"]:
+            subject = asset["path"].split("/")[0]
+            rid = f"dandi-{identifier}-{subject}"
+            animal = f"dandi:{identifier}:{subject}"
+            subject_groups.setdefault(subject, []).append(rid)
+            full_name = f"{identifier}/{asset['path']}"
+            split = split_lookup.loc[full_name] if full_name in split_lookup.index else None
+            tracking = bool(split.use_for_calcium_task == 1) if split is not None else False
+            # SF is explicitly excluded from the published trusted tracking subset.
+            tracking = tracking and cohort != "SF"
+            record = {
+                "recording_id": rid,
+                "source_dataset": f"dandi-{identifier}",
+                "source_cohort": cohort,
+                "source_version": assets["version"],
+                "source_asset": asset["asset_id"],
+                "source_path": asset["path"],
+                "source_checksum": json.dumps(provenance.get(asset["asset_id"], {}).get("digest")),
+                "source_size_bytes": asset["size"],
+                "lab_id": None,
+                "animal_id": animal,
+                "session_id": asset["path"].split("/")[-1].removesuffix(".nwb"),
+                "species": None,
+                "sex": None,
+                "stage": None,
+                "strain": None,
+                "fov_region": "unknown",
+                "acquisition_setup": "unknown",
+                "shape_tczyx": None,
+                "voxel_size_um": None,
+                "timestamps_status": "not_inspected",
+                "channels": None,
+                "tracking_gt_level": "not_inspected",
+                "posture_gt_level": "not_inspected",
+                "source_overlap_group": subject,
+                "license": ";".join(release.get("license", [])),
+                "qc_status": "inventory_only",
+                "hosted_tracks": False,
+                "hosted_volume": False,
+                "wormid_tracking_subset": tracking,
+                "wormid_fold": str(split.dataset_split) if split is not None else None,
+            }
+            recordings.append(record)
+            upstream.append(
+                {
+                    "recording_id": rid,
+                    "asset_id": asset["asset_id"],
+                    "source_version": assets["version"],
+                    "path": asset["path"],
+                    "bytes": asset["size"],
+                    "digests": provenance.get(asset["asset_id"], {}).get("digest"),
+                    "location": "external",
+                    "url": f"https://api.dandiarchive.org/api/assets/{asset['asset_id']}/download/",
+                }
+            )
+    for subject, ids in subject_groups.items():
+        if len(ids) > 1:
+            overlaps.append(
+                {
+                    "group_id": subject,
+                    "recording_ids": ids,
+                    "status": "potential_same_animal_or_acquisition",
+                    "evidence": "shared upstream subject identifier",
+                    "split_policy": "keep together; prohibit separate test assignments until audited",
+                }
+            )
+    components, qc, counts = {}, {}, {}
+
+    def recording(rid):
+        return next(r for r in recordings if r["recording_id"] == rid)
+
+    def register_tracks(rid, df, shape_xyz):
+        qc[rid] = validate_tracks(df, shape_xyz=shape_xyz)
+        qc[rid]["invalid_observations_preserved"] = int((~df.validity).sum())
+        counts[rid] = qc[rid]
+        entry = components.setdefault(rid, {})
+        entry["tracks"] = table(root, f"data/tracks/{rid}.parquet", df)
+        detections = df.drop(columns=["track_id"]).copy()
+        entry["detections"] = table(root, f"data/detections/{rid}.parquet", detections)
+        recording(rid).update(
+            hosted_tracks=True,
+            qc_status="table_validated",
+            tracking_gt_level="trusted_human_corrected"
+            if set(df.annotation_type) == {"human_corrected"}
+            else "unknown_or_mixed_provenance",
+        )
+
+    # Small real recordings and trusted ASCENT annotations.
+    for source, identifier in [
+        ("ASCENT", "17561700"),
+        ("ZephIR", "10818810"),
+        ("Targettrack", "10008744"),
+    ]:
+        meta = json.loads((audit / f"zenodo-{identifier}.json").read_text())
+        sources.append(
+            {
+                "source_id": source.lower(),
+                "name": source,
+                "priority": "P0" if source == "ZephIR" else "P1",
+                "version": f"10.5281/zenodo.{identifier}",
+                "url": f"https://zenodo.org/records/{identifier}",
+                "license": meta["metadata"].get("license", {}).get("id"),
+                "license_status": "verified_record_metadata",
+                "asset_count": len(meta["files"]),
+                "bytes": sum(a["size"] for a in meta["files"]),
+                "assembly_status": "pilot_assets_and_tables",
+            }
+        )
+        for asset in meta["files"]:
+            upstream.append(
+                {
+                    "recording_id": None,
+                    "source_dataset": source.lower(),
+                    "path": asset["key"],
+                    "bytes": asset["size"],
+                    "digests": {"md5": asset["checksum"].split(":", 1)[-1]},
+                    "location": "external",
+                    "url": asset["links"]["self"],
+                }
+            )
+            local = raw / asset["key"]
+            if local.exists() and digest(local, "md5") != asset["checksum"].split(":", 1)[-1]:
+                raise ValueError(f"Upstream checksum mismatch: {local.name}")
+    template = {
+        "source_checksum": None,
+        "source_size_bytes": None,
+        "species": "Caenorhabditis elegans",
+        "sex": None,
+        "stage": None,
+        "strain": None,
+        "fov_region": "unknown",
+        "acquisition_setup": "unknown",
+        "shape_tczyx": None,
+        "voxel_size_um": None,
+        "timestamps_status": "unknown",
+        "channels": None,
+        "tracking_gt_level": "unknown",
+        "posture_gt_level": "unavailable",
+        "license": "CC-BY-4.0",
+        "qc_status": "inventory_only",
+        "hosted_tracks": False,
+        "hosted_volume": False,
+        "wormid_tracking_subset": False,
+        "wormid_fold": None,
+    }
+
+    def new_record(rid, source, version, asset, **kwargs):
+        record = dict(
+            template,
+            recording_id=rid,
+            source_dataset=source,
+            source_cohort=source,
+            source_version=version,
+            source_asset=asset,
+            source_path=asset,
+            lab_id=None,
+            animal_id=rid,
+            session_id=rid,
+            source_overlap_group=rid,
+        )
+        record.update(kwargs)
+        recordings.append(record)
+
+    rid = "ascent-opterra"
+    new_record(
+        rid,
+        "ascent",
+        "10.5281/zenodo.17561700",
+        "InHouse_Opterra_tracks_GT.csv",
+        fov_region="head",
+        acquisition_setup="device_restricted",
+        strain="ZM9624",
+        shape_tczyx=json.dumps([1100, 2, 9, 512, 512]),
+        voxel_size_um=json.dumps([0.243, 0.243, 1.5]),
+        timestamps_status="nominal_3.3Hz_not_measured_timestamps",
+        channels=json.dumps(
+            [
+                {"index": 0, "name": "mNeptune", "role": "structural"},
+                {"index": 1, "name": "GCaMP6", "role": "calcium"},
+            ]
+        ),
+    )
+    clean = ascent_tracks(raw / "InHouse_Opterra_tracks_GT.csv", rid)
+    register_tracks(rid, clean, [512, 512, 9])
+    pred = ascent_tracks(raw / "InHouse_Opterra_tracks_PRED.csv", rid, predicted=True)
+    qc["ascent-source-predictions"] = validate_tracks(pred, shape_xyz=[512, 512, 9])
+    table(root, "evaluation/source_predictions/ascent.parquet", pred)
+    # Source-predicted objects differ from oracle detections: do not score these as oracle association.
+    ref = clean.loc[clean.frame_index == clean.frame_index.min()].copy()
+    ref["reference_origin"] = "same_animal_human_corrected_reference_frame"
+    components[rid]["anatomy"] = table(root, f"data/anatomy/{rid}.parquet", ref)
+    scenarios, verification, results = [], [], {}
+    clean_control = clean.copy()
+    clean_control["scenario_id"] = "clean"
+    clean_control["is_error"] = False
+    clean_control["reference_track_id"] = clean_control.track_id
+    clean_control["corruption_type"] = "clean"
+    clean_control["seed"] = SEED
+    scenarios.append(clean_control)
+    # Choose a nearby pair using only the development reference frame.
+    positions = ref[["x_um", "y_um", "z_um"]].to_numpy()
+    distance = np.linalg.norm(positions[:, None] - positions[None], axis=2)
+    np.fill_diagonal(distance, np.inf)
+    i, j = np.unravel_index(distance.argmin(), distance.shape)
+    pair = [ref.track_id.iloc[i], ref.track_id.iloc[j]]
+    for kind in [
+        "single_frame_swap",
+        "persistent_swap",
+        "swap_recovery",
+        "nearby_swap",
+        "fragmentation",
+        "dropout",
+    ]:
+        stop = 700 if kind in {"swap_recovery", "nearby_swap"} else None
+        corrupted = corrupt_tracks(
+            clean,
+            kind=kind,
+            seed=SEED,
+            start_frame=300,
+            stop_frame=stop,
+            pair=pair if kind == "nearby_swap" else None,
+        )
+        scenarios.append(corrupted)
+    for df in scenarios:
+        scores = reference_residual_scores(df, ref)
+        report = verifier_metrics(scores, df.is_error, threshold=5.0)
+        # Keep exact risk/coverage breakpoints in a separate tabular artifact.
+        curve = report.pop("risk_coverage")
+        table(
+            root, f"evaluation/risk_coverage/{df.scenario_id.iloc[0]}.parquet", pd.DataFrame(curve)
+        )
+        scenario = df.scenario_id.iloc[0]
+        report["association"] = association_metrics(clean, df)
+        results[scenario] = report
+        verification.append(
+            pd.DataFrame(
+                {
+                    "recording_id": rid,
+                    "scenario_id": scenario,
+                    "frame_index": df.frame_index,
+                    "detection_id": df.detection_id,
+                    "reference_track_id": df.reference_track_id,
+                    "proposed_track_id": df.track_id,
+                    "is_error": df.is_error,
+                    "score_um": scores,
+                    "verifier": "fixed_reference_distance",
+                    "threshold_um": 5.0,
+                    "partition": "development",
+                }
+            )
+        )
+    components[rid]["perturbations"] = table(
+        root, f"data/perturbations/{rid}.parquet", pd.concat(scenarios, ignore_index=True)
+    )
+    table(root, f"data/verification/{rid}.parquet", pd.concat(verification, ignore_index=True))
+    for method in ["nearest_neighbor", "hungarian"]:
+        prediction = oracle_association_baseline(clean.drop(columns="track_id"), method=method)
+        table(root, f"evaluation/baseline_predictions/{method}.parquet", prediction)
+        results[method] = association_metrics(clean, prediction)
+    dump(root / "evaluation/pilot_results.json", results)
+
+    for name, rid, source in [
+        ("ZM9624_Ex(myo-20_mCherry)", "zephir-zm9624", "zephir"),
+        ("nguyen2017_AL_w1", "nerve-al-w1-via-zephir", "nerve"),
+    ]:
+        folder = raw / "zephir/zephir" / name
+        meta = json.loads((folder / "metadata.json").read_text())
+        new_record(
+            rid,
+            source,
+            "10.5281/zenodo.10818810",
+            f"zephir/{name}/annotations.h5",
+            acquisition_setup="freely_moving",
+            shape_tczyx=json.dumps([meta[f"shape_{a}"] for a in "tczyx"]),
+            source_overlap_group="nerve-al-w1" if source == "nerve" else rid,
+            timestamps_status="source_times_hosted" if source == "zephir" else "unknown",
+        )
+        register_tracks(
+            rid,
+            zephir_tracks(folder / "annotations.h5", meta, rid),
+            [meta[f"shape_{a}"] for a in "xyz"],
+        )
+        dest = root / "assets" / rid
+        dest.mkdir(parents=True, exist_ok=True)
+        for filename in ["annotations.h5", "worldlines.h5", "metadata.json"]:
+            shutil.copy2(folder / filename, dest / filename)
+    if (audit / "zephir-image-times.json").exists():
+        shutil.copy2(
+            audit / "zephir-image-times.json", root / "assets/zephir-zm9624/timestamps_s.json"
+        )
+
+    rid = "targettrack-epfl10"
+    new_record(
+        rid,
+        "targettrack",
+        "10.5281/zenodo.10008744",
+        "Targettrack/epfl10_CZANet_Final.h5",
+        shape_tczyx=json.dumps([761, 1, 32, 128, 192]),
+        acquisition_setup="unknown",
+        tracking_gt_level="mask_provenance_unresolved",
+    )
+    points, transforms = [], []
+    with h5py.File(raw / "Targettrack/epfl10_CZANet_Final.h5") as h:
+        for t in range(int(h.attrs["T"])):
+            g = h[str(t)]
+            transforms.append(
+                {
+                    "recording_id": rid,
+                    "frame_index": t,
+                    "original_frame_index": int(g["original_fr"][()]),
+                    "source_transform": g["transfo_matrix"][()].reshape(-1).tolist(),
+                    "direction": "unknown",
+                    "units": "unknown",
+                }
+            )
+            if "mask" not in g:
+                continue
+            mask = g["mask"][()]
+            for identity in np.unique(mask):
+                if identity == 0:
+                    continue
+                xyz = np.argwhere(mask == identity).mean(axis=0)
+                points.append(
+                    {
+                        "recording_id": rid,
+                        "frame_index": t,
+                        "track_id": str(identity),
+                        "detection_id": f"{t}:{identity}",
+                        **dict(zip(XYZ, xyz.tolist(), strict=True)),
+                        "x_um": np.nan,
+                        "y_um": np.nan,
+                        "z_um": np.nan,
+                        "annotation_type": "unknown",
+                        "annotation_confidence": np.nan,
+                        "label_set": "source_mask",
+                        "source_provenance": "centroid_of_native_mask; manual_or_propagated_unresolved",
+                        "coordinate_system": "source_processed_CXYZ",
+                        "validity": True,
+                    }
+                )
+    register_tracks(rid, pd.DataFrame(points), [192, 128, 32])
+    table(root, "metadata/targettrack_transforms.parquet", pd.DataFrame(transforms))
+
+    # Same-animal NeuroPAL cloud from a directly inspected CC-BY NWB.
+    asset = json.loads((audit / "dandi-000715-assets.json").read_text())["assets"][0]
+    rid = "dandi-000715-" + asset["path"].split("/")[0]
+    with h5py.File(raw / "neuropal-reference.nwb") as h:
+        group = h["processing/NeuroPAL/NeuroPALSegmentation/NeuroPALNeurons"]
+        voxels, ids = group["voxel_mask"][()], group["id"][()]
+        spacing = h["general/optophysiology/NeuroPALImVol/grid_spacing"]
+        if spacing.attrs["unit"] != "micrometers":
+            raise ValueError("Unsupported physical calibration")
+        cloud = pd.DataFrame(
+            {
+                "recording_id": rid,
+                "track_id": ids.astype(str),
+                **{f"{a}_voxel": voxels[a].astype(float) for a in "xyz"},
+                **{f"{a}_um": voxels[a] * spacing[i] for i, a in enumerate("xyz")},
+                "reference_origin": "same_animal_structural_volume",
+                "annotation_type": "source_annotation_uncertainty_unknown",
+                "coordinate_system": "source_microscope",
+                "uncertainty_um": np.nan,
+            }
+        )
+        components.setdefault(rid, {})["anatomy"] = table(
+            root, f"data/anatomy/{rid}.parquet", cloud
+        )
+        chars = group["ID_labels"][()]
+        ends = group["ID_labels_index"][()]
+        start, names = 0, []
+        for end in ends:
+            names.append(b"".join(chars[start:end]).decode())
+            start = end
+        table(
+            root,
+            "metadata/optional_neuronal_names.parquet",
+            pd.DataFrame(
+                {
+                    "recording_id": rid,
+                    "source_neuron_id": ids.astype(str),
+                    "biological_name": names,
+                    "used_in_primary_evaluation": False,
+                }
+            ),
+        )
+        recording(rid).update(
+            qc_status="reference_cloud_validated", voxel_size_um=json.dumps(spacing[()].tolist())
+        )
+    # Preserve the original small structural NWB; explicit component download only.
+    (root / "assets" / rid).mkdir(parents=True, exist_ok=True)
+    shutil.copy2(raw / "neuropal-reference.nwb", root / "assets" / rid / "reference.nwb")
+
+    if (raw / "wormid-ey-pilot.parquet").exists():
+        meta = json.loads((audit / "wormid-ey-pilot.json").read_text())
+        rid = "dandi-000541-" + meta["asset"]["path"].split("/")[0]
+        df = pd.read_parquet(raw / "wormid-ey-pilot.parquet")
+        for i, a in enumerate("xyz"):
+            df[f"{a}_um"] = df[f"{a}_voxel"] * meta["spacing_xyz"][i]
+        df["recording_id"], df["annotation_type"], df["label_set"] = (
+            rid,
+            "unknown",
+            "source_segmentation",
+        )
+        df["annotation_confidence"] = np.nan
+        df["source_provenance"] = "native_NWB_plane_segmentation_ID; temporal_identity_QC_pending"
+        df["coordinate_system"], df["validity"] = "source_microscope", True
+        register_tracks(rid, df, list(reversed(meta["shape_tczyx"][2:])))
+        recording(rid).update(
+            shape_tczyx=json.dumps(meta["shape_tczyx"]),
+            voxel_size_um=json.dumps(meta["spacing_xyz"]),
+            timestamps_status="starting_time_and_rate_4Hz",
+            fov_region="unknown",
+        )
+        dump(root / "metadata/wormid_ey_image_annotation_discrepancy.json", meta)
+    if (raw / "sf-behavior.parquet").exists():
+        meta = json.loads((audit / "sf-behavior.json").read_text())
+        rid = "dandi-000776-" + meta["asset"]["path"].split("/")[0]
+        df = pd.read_parquet(raw / "sf-behavior.parquet")
+        df["recording_id"], df["component_type"], df["status"] = rid, "behavior_summary", "measured"
+        components.setdefault(rid, {})["posture"] = table(root, f"data/posture/{rid}.parquet", df)
+        recording(rid)["posture_gt_level"] = "behavioral_summaries_only_no_centerline"
+        dump(root / "metadata/sf_behavior_provenance.json", meta)
+    for stem, rid, frames in [
+        ("ascent-opterra", "ascent-opterra", list(range(4))),
+        ("zephir-zm9624", "zephir-zm9624", list(range(4))),
+        ("targettrack-epfl10", "targettrack-epfl10", list(range(32))),
+        ("wormid-ey-20190924-01", "dandi-000541-sub-20190924-01", list(range(4))),
+    ]:
+        src = workspace / "volumes" / (stem + ".h5")
+        if src.exists():
+            dest = root / "assets" / rid / "pilot.h5"
+            dest.parent.mkdir(parents=True, exist_ok=True)
+            shutil.copy2(src, dest)
+            components.setdefault(rid, {})["volume"] = {
+                "path": str(dest.relative_to(root)),
+                "frames": frames,
+                "axis_order": "CZYX",
+                "transformation": "axis_permutation_only",
+            }
+            recording(rid)["hosted_volume"] = True
+    for source, name, priority, url, status in [
+        (
+            "nerve",
+            "NeRVE original release",
+            "P0",
+            "https://doi.org/10.21227/H2901H",
+            "one_annotation_recording_via_ZephIR; original_inventory_pending",
+        ),
+        (
+            "cender",
+            "CeNDeR C1-C3",
+            "P0",
+            "https://osf.io/v2b5n/",
+            "blocked_data_license_unstated; OSF_storage_empty; linked_Google_Drive_API_404",
+        ),
+        (
+            "brainalignnet",
+            "BrainAlignNet / ANTSUN 2.0",
+            "P1",
+            "https://doi.org/10.7910/DVN/8UE0H9",
+            "Dataverse_API_unavailable; license_and_overlap_pending",
+        ),
+        (
+            "skuhersky-atlas",
+            "Skuhersky 3D anatomical atlas",
+            "P2",
+            "https://github.com/bluevex/elegans-atlas",
+            "data_license_unresolved; external_reference_only",
+        ),
+        (
+            "neuropal-canonical",
+            "NeuroPAL canonical positions",
+            "P2",
+            "https://github.com/openworm/NeuroPAL",
+            "shared_Skuhersky_CSV; data_license_unresolved",
+        ),
+        (
+            "stat-atlas",
+            "Statistical anatomical atlas",
+            "P2",
+            "https://github.com/amin-nejat/stat-atlas",
+            "data_license_unresolved; external_reference_only",
+        ),
+        (
+            "openworm-movement",
+            "OpenWorm Movement Database",
+            "P2",
+            "https://movement.openworm.org/",
+            "DNS_resolution_failed; license_and_assets_unresolved",
+        ),
+        (
+            "eigenworms",
+            "Stephens et al. 2008",
+            "P2",
+            "https://doi.org/10.1371/journal.pcbi.1000028",
+            "paper_audited; reusable_basis_asset_not_ingested",
+        ),
+    ]:
+        sources.append(
+            {
+                "source_id": source,
+                "name": name,
+                "priority": priority,
+                "url": url,
+                "version": None,
+                "license": None,
+                "license_status": "verify",
+                "assembly_status": status,
+                "asset_count": None,
+                "bytes": None,
+            }
+        )
+    # All pilots are development assets. No small single-worm split is presented as generalization.
+    active = sorted(components)
+    splits = {
+        p: {"train": [], "validation": [], "test": [], "development": active, "quarantine": []}
+        for p in ["tracking_gt", "anatomical_verification", "anatomical_consistency"]
+    }
+    splits["tracking_gt"]["development"] = ["ascent-opterra"]
+    splits["anatomical_verification"]["development"] = ["ascent-opterra"]
+    table(root, "data/recordings/recordings.parquet", pd.DataFrame(recordings))
+    dump(root / "metadata/sources.json", sources)
+    dump(root / "metadata/upstream_assets.json", upstream)
+    dump(root / "metadata/overlap_registry.json", overlaps)
+    dump(
+        root / "metadata/client_manifest.json",
+        {
+            "schema_version": VERSION,
+            "recordings": "data/recordings/recordings.parquet",
+            "components": components,
+            "splits": splits,
+        },
+    )
+    dump(
+        root / "splits/assignments.json",
+        {
+            "seed": SEED,
+            "status": "development_only_no_frozen_test",
+            "protocols": splits,
+            "original_wormid_commit": WORMND_COMMIT,
+        },
+    )
+    table(root, "splits/original_wormid_folds.parquet", original)
+    dump(
+        root / "evaluation/validation.json",
+        {"status": "passed", "scope": "published_table_invariants", "tables": qc},
+    )
+    dump(
+        root / "metadata/assembly_counts.json",
+        {
+            "version": VERSION,
+            "assembled_at": now,
+            "inventory_recordings": len(recordings),
+            "wormid_inventory_assets": sum(
+                r["source_dataset"] in {f"dandi-{i}" for i in list(DANDI_COHORTS)[:7]}
+                for r in recordings
+            ),
+            "ingested": counts,
+            "source_inventory_entries": len(sources),
+            "pilot_imaging_frames": sum(
+                len(c.get("volume", {}).get("frames", [])) for c in components.values()
+            ),
+            "anatomical_clouds": sum("anatomy" in c for c in components.values()),
+            "measured_centerlines": 0,
+            "independent_behavior_summary_recordings": sum(
+                "posture" in c for c in components.values()
+            ),
+            "release_gates": {
+                "A": "partial: full non-DANDI inventories pending",
+                "B": "partial: independent moving-worm GT and centerline pending",
+                "C": "not_passed: all P0 ingestion and held-out evaluation pending",
+            },
+        },
+    )
+    harmonize_tables(root)
+    print(
+        json.dumps(
+            {"root": str(root), "inventory_recordings": len(recordings), "ingested": counts},
+            indent=2,
+        )
+    )
+    return root
+
+
+if __name__ == "__main__":
+    build(*sys.argv[1:3])
diff --git a/src/cell_tracking/data/wormtrack_verify/client.py b/src/cell_tracking/data/wormtrack_verify/client.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ca3db8d43ea907cd48c5dfef60668cf3f71f176
--- /dev/null
+++ b/src/cell_tracking/data/wormtrack_verify/client.py
@@ -0,0 +1,131 @@
+"""Recording-selective Hub access; metadata never triggers volume acquisition."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+
+class MissingComponentError(LookupError):
+    """A requested component is unavailable, rather than an empty measurement."""
+
+
+class BenchmarkClient:
+    """Read a pinned release or local assembly with a per-file download budget.
+
+    Tables retain native XYZ voxel coordinates. Physical XYZ fields are null if
+    calibration is unknown. Volumes return CZYX, without intensity normalization.
+    Local assemblies support the same API as remote commits. Remote callers must
+    explicitly select a revision; this prevents silent changes in experiments.
+    """
+
+    def __init__(
+        self,
+        repo_id="pytc/trackingBench",
+        *,
+        revision=None,
+        root=None,
+        cache_dir=None,
+        max_download_bytes=128 * 1024**2,
+    ):
+        if root is None and revision is None:
+            raise ValueError("Pin a Hub commit/tag with revision, or supply root")
+        self.repo_id = repo_id
+        self.revision = revision
+        self.root = Path(root) if root is not None else None
+        self.cache_dir = cache_dir
+        self.max_download_bytes = max_download_bytes
+        self.manifest = json.loads(self._file("metadata/client_manifest.json").read_text())
+
+    def _file(self, path):
+        logical = Path(path)
+        if logical.is_absolute() or ".." in logical.parts:
+            raise ValueError("Manifest paths must stay within the repository")
+        if self.root is not None:
+            return self.root / logical
+        from huggingface_hub import HfApi, hf_hub_download
+
+        info = next(
+            iter(
+                HfApi().get_paths_info(
+                    self.repo_id, [path], repo_type="dataset", revision=self.revision
+                )
+            ),
+            None,
+        )
+        if info is None or getattr(info, "size", None) is None:
+            raise MissingComponentError(path)
+        if info.size > self.max_download_bytes:
+            raise ValueError(f"{path} exceeds the {self.max_download_bytes}-byte file budget")
+        return Path(
+            hf_hub_download(
+                self.repo_id,
+                path,
+                repo_type="dataset",
+                revision=self.revision,
+                cache_dir=self.cache_dir,
+            )
+        )
+
+    def list_recordings(self, **filters):
+        import pandas as pd
+
+        df = pd.read_parquet(self._file(self.manifest["recordings"]))
+        for key, value in filters.items():
+            df = df.loc[df[key] == value]
+        return df.reset_index(drop=True)
+
+    def _component(self, recording_id, component):
+        import pandas as pd
+
+        entry = self.manifest["components"].get(recording_id)
+        if entry is None:
+            raise MissingComponentError(f"Unknown recording: {recording_id}")
+        path = entry.get(component)
+        if path is None:
+            raise MissingComponentError(f"{component} unavailable for {recording_id}")
+        return pd.read_parquet(self._file(path))
+
+    def load_detections(self, recording_id):
+        return self._component(recording_id, "detections")
+
+    def load_tracks(self, recording_id, label_source=None):
+        df = self._component(recording_id, "tracks")
+        if label_source is not None:
+            df = df.loc[df.annotation_type == label_source].reset_index(drop=True)
+        return df
+
+    def load_reference_cloud(self, recording_id):
+        return self._component(recording_id, "anatomy")
+
+    def load_posture(self, recording_id):
+        return self._component(recording_id, "posture")
+
+    def load_corruptions(self, recording_id):
+        return self._component(recording_id, "perturbations")
+
+    def get_split(self, protocol, partition):
+        if protocol not in {"tracking_gt", "anatomical_verification", "anatomical_consistency"}:
+            raise ValueError(f"Unknown protocol: {protocol}")
+        if partition not in {"train", "validation", "test", "development", "quarantine"}:
+            raise ValueError(f"Unknown partition: {partition}")
+        return self.manifest["splits"][protocol].get(partition, [])
+
+    def load_volume(self, recording_id, frame, channels=None):
+        """Download only the small declared imaging pilot and return one CZYX frame.
+
+        Frames outside the hosted pilot raise MissingComponentError with upstream
+        resolution details. Original multi-GB recordings are never auto-downloaded.
+        """
+        import h5py
+        import numpy as np
+
+        entry = self.manifest["components"].get(recording_id, {})
+        volume = entry.get("volume")
+        if volume is None or frame not in volume["frames"]:
+            raise MissingComponentError(
+                f"No hosted frame {frame} for {recording_id}; consult metadata/upstream_assets.json"
+            )
+        with h5py.File(self._file(volume["path"]), "r") as h:
+            data = h[f"frames/{frame}"][()]
+        return data if channels is None else np.take(data, channels, axis=0)
diff --git a/src/cell_tracking/data/wormtrack_verify/inventory.py b/src/cell_tracking/data/wormtrack_verify/inventory.py
new file mode 100644
index 0000000000000000000000000000000000000000..208258138224b91e5edb29e76330c8a62cec3bd3
--- /dev/null
+++ b/src/cell_tracking/data/wormtrack_verify/inventory.py
@@ -0,0 +1,102 @@
+"""Pin official source metadata without bulk acquisition."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import requests
+
+DANDI_COHORTS = {
+    "000541": "EY",
+    "000692": "KK",
+    "000565": "SK1",
+    "000472": "SK2",
+    "000776": "SF",
+    "000715": "NP",
+    "000714": "HL",
+    "001623": "Dunn",
+    "000981": "Chemosensory",
+}
+ZENODO = {"17561700": "ASCENT", "10818810": "ZephIR", "10008744": "Targettrack"}
+WORMND_COMMIT = "0c74e9ddffee138cfc6330ab65c0e690e8e199dd"
+
+
+def inventory(workspace):
+    """Save release metadata, immutable asset IDs/checksums, and the original split CSV.
+
+    Re-running an inventory captures the current latest upstream release. Assembly
+    uses saved snapshots, never silently updates an existing experiment. Original
+    data licenses are taken from release metadata, separately from code licenses.
+    """
+    out = Path(workspace) / "audit"
+    out.mkdir(parents=True, exist_ok=True)
+    asset_provenance = {}
+    for identifier in DANDI_COHORTS:
+        base = f"https://api.dandiarchive.org/api/dandisets/{identifier}/versions/"
+        versions = requests.get(base, timeout=60)
+        versions.raise_for_status()
+        versions = versions.json()
+        versions = versions.get("results", versions) if isinstance(versions, dict) else versions
+        published = [v["version"] for v in versions if v["version"] != "draft"]
+        version = max(published) if published else "draft"
+        metadata = requests.get(base + version + "/", timeout=60)
+        metadata.raise_for_status()
+        (out / f"dandi-{identifier}-pinned.json").write_text(json.dumps(metadata.json(), indent=2))
+        assets = []
+        url = base + version + "/assets/?page_size=1000"
+        while url:
+            response = requests.get(url, timeout=60)
+            response.raise_for_status()
+            page = response.json()
+            assets.extend(page["results"])
+            url = page.get("next")
+        (out / f"dandi-{identifier}-assets.json").write_text(
+            json.dumps({"version": version, "assets": assets}, indent=2)
+        )
+        for asset in assets:
+            response = requests.get(
+                f"https://api.dandiarchive.org/api/assets/{asset['asset_id']}/", timeout=60
+            )
+            response.raise_for_status()
+            value = response.json()
+            asset_provenance[asset["asset_id"]] = {
+                key: value.get(key)
+                for key in ["digest", "wasAttributedTo", "wasGeneratedBy", "datePublished"]
+            }
+    (out / "dandi-asset-provenance.json").write_text(json.dumps(asset_provenance, indent=2))
+    for identifier in ZENODO:
+        response = requests.get(f"https://zenodo.org/api/records/{identifier}", timeout=60)
+        response.raise_for_status()
+        (out / f"zenodo-{identifier}.json").write_text(json.dumps(response.json(), indent=2))
+    url = (
+        f"https://raw.githubusercontent.com/focolab/WormND/{WORMND_COMMIT}"
+        "/neuron_tracking/3deecelltracker/dataset_split.csv"
+    )
+    response = requests.get(url, timeout=60)
+    response.raise_for_status()
+    (out / "wormid-tracking-split.csv").write_text(response.text)
+
+
+def snapshot_hub(workspace, repo_id="pytc/trackingBench"):
+    """Record the immutable Hub state before edits; does not publish or expose tokens."""
+    from huggingface_hub import HfApi, hf_hub_download
+
+    api = HfApi()
+    info = api.dataset_info(repo_id, files_metadata=True)
+    readme = Path(
+        hf_hub_download(repo_id, "README.md", repo_type="dataset", revision=info.sha)
+    ).read_text()
+    snapshot = {
+        "repo_id": repo_id,
+        "sha": info.sha,
+        "private": info.private,
+        "card": info.card_data.to_dict() if info.card_data else {},
+        "readme": readme,
+        "files": [
+            {"path": f.rfilename, "size": f.size, "blob_id": f.blob_id} for f in info.siblings
+        ],
+    }
+    Path(workspace).mkdir(parents=True, exist_ok=True)
+    (Path(workspace) / "hub-before.json").write_text(json.dumps(snapshot, indent=2))
+    return snapshot
diff --git a/src/cell_tracking/evaluation/__init__.py b/src/cell_tracking/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5eb8a428df5ef22e0fe99633570b2be77ae84d3
--- /dev/null
+++ b/src/cell_tracking/evaluation/__init__.py
@@ -0,0 +1 @@
+"""WormTrack-Verify benchmark helpers."""
diff --git a/src/cell_tracking/evaluation/wormtrack_verify.py b/src/cell_tracking/evaluation/wormtrack_verify.py
new file mode 100644
index 0000000000000000000000000000000000000000..525411608bc8c40d99e309cec1800db4a84664f2
--- /dev/null
+++ b/src/cell_tracking/evaluation/wormtrack_verify.py
@@ -0,0 +1,245 @@
+"""Explicit pilot diagnostics for association-only corruption and abstention.
+
+These metrics are defined in the benchmark specification, not replacements for
+canonical HOTA/IDF1. Detections stay fixed. One global identity alignment is allowed.
+"""
+
+from __future__ import annotations
+
+import itertools
+
+import numpy as np
+import pandas as pd
+from scipy.optimize import linear_sum_assignment
+from sklearn.metrics import average_precision_score, roc_auc_score
+
+from cell_tracking.data.wormtrack_verify.adapters import TRUSTED, XYZ, validate_tracks
+
+
+def corrupt_tracks(tracks, *, kind, seed, start_frame, stop_frame=None, pair=None):
+    """Deterministic swaps/fragmentation with row-level error labels and fixed observations.
+
+    Labels mark correspondence to the original reference identity. Purely global
+    renaming is not an error; start_frame must follow the first reference frame.
+    Original images, detection IDs, and coordinates are preserved.
+    """
+    validate_tracks(tracks)
+    if not set(tracks.annotation_type).issubset(TRUSTED):
+        raise ValueError("Corruptions require independently trusted source annotations")
+    if tracks.recording_id.nunique() != 1 or tracks.label_set.nunique() != 1:
+        raise ValueError("Corrupt one recording and one label set at a time")
+    frames = sorted(tracks.frame_index.unique())
+    if start_frame <= frames[0] or start_frame > frames[-1]:
+        raise ValueError("Corruption must follow the identity anchor and lie inside the recording")
+    stop_frame = frames[-1] + 1 if stop_frame is None else stop_frame
+    if stop_frame <= start_frame or stop_frame > frames[-1] + 1:
+        raise ValueError("Invalid corruption interval")
+    rng = np.random.default_rng(seed)
+    ids = sorted(tracks.track_id.unique())
+    if pair is None:
+        if len(ids) < 2:
+            raise ValueError("Need two tracks")
+        pair = list(rng.choice(ids, 2, replace=False))
+    if len(pair) != 2 or pair[0] == pair[1] or not set(pair).issubset(ids):
+        raise ValueError("Pair must identify two distinct source tracks")
+    df = tracks.copy()
+    df["reference_track_id"] = df.track_id
+    interval = (df.frame_index >= start_frame) & (df.frame_index < stop_frame)
+    affected = interval & df.track_id.isin(pair)
+    if not affected.any():
+        raise ValueError("No observations in the selected corruption interval")
+    if kind in {"single_frame_swap", "persistent_swap", "swap_recovery", "nearby_swap"}:
+        if kind == "single_frame_swap":
+            affected &= df.frame_index == start_frame
+        df.loc[affected, "track_id"] = df.loc[affected, "track_id"].map(
+            {pair[0]: pair[1], pair[1]: pair[0]}
+        )
+    elif kind == "fragmentation":
+        affected = interval & (df.track_id == pair[0])
+        df.loc[affected, "track_id"] = pair[0] + f":fragment:{seed}"
+    elif kind == "dropout":
+        affected = interval & (df.track_id == pair[0])
+        df.loc[affected, "validity"] = False
+    else:
+        raise ValueError(f"Unsupported pilot corruption: {kind}")
+    df["is_error"] = affected
+    df["corruption_type"] = kind
+    df["seed"] = seed
+    df["scenario_id"] = f"{kind}:seed{seed}"
+    return df
+
+
+def association_metrics(reference, prediction):
+    """Score shared oracle detections with one recording-wide identity permutation.
+
+    Missing predictions count against coverage and correct-observation rate.
+    Switches are changes in predicted identity along each reference trajectory;
+    fragmented runs also count reappearance after a missing prediction. Evaluation
+    covers only source-supported reference observations, not unannotated frames.
+    """
+    validate_tracks(reference)
+    validate_tracks(prediction)
+    if reference.recording_id.nunique() != 1 or prediction.recording_id.nunique() != 1:
+        raise ValueError("Evaluate each independent recording separately")
+    if reference.recording_id.iloc[0] != prediction.recording_id.iloc[0]:
+        raise ValueError("Mismatched recording")
+    key = ["frame_index", "detection_id"]
+    valid_prediction = prediction.loc[prediction.validity]
+    joined = reference.loc[reference.validity].merge(
+        valid_prediction[key + ["track_id"]],
+        on=key,
+        how="left",
+        suffixes=("_gt", "_pred"),
+        validate="one_to_one",
+    )
+    gt = sorted(joined.track_id_gt.unique())
+    pred = sorted(valid_prediction.track_id.unique())
+    counts = (
+        pd.crosstab(joined.track_id_gt, joined.track_id_pred)
+        .reindex(index=gt, columns=pred, fill_value=0)
+        .to_numpy()
+    )
+    mapping = {}
+    if counts.size:
+        rows, cols = linear_sum_assignment(-counts)
+        mapping = {pred[c]: gt[r] for r, c in zip(rows, cols, strict=True)}
+    correct = joined.track_id_pred.map(mapping).eq(joined.track_id_gt)
+    switches = fragments = 0
+    for _, group in joined.sort_values("frame_index").groupby("track_id_gt"):
+        ids = group.track_id_pred.tolist()
+        observed = [i for i in ids if pd.notna(i)]
+        switches += sum(a != b for a, b in itertools.pairwise(observed))
+        fragments += max(
+            0,
+            sum(
+                pd.notna(v) and (j == 0 or pd.isna(ids[j - 1]) or v != ids[j - 1])
+                for j, v in enumerate(ids)
+            )
+            - 1,
+        )
+    extra = len(
+        valid_prediction.merge(joined[key], on=key, how="left", indicator=True).query(
+            '_merge == "left_only"'
+        )
+    )
+    return {
+        "reference_observations": len(joined),
+        "predicted_observations": len(valid_prediction),
+        "coverage": float(joined.track_id_pred.notna().mean()),
+        "correct_observation_rate": float(correct.mean()),
+        "identity_switches": int(switches),
+        "fragmentations": int(fragments),
+        "extra_observations": int(extra),
+    }
+
+
+def reference_residual_scores(tracks, reference_cloud, *, physical=True):
+    """Identity-sensitive distance from the fixed first-frame reference.
+
+    This intentionally simple consistency diagnostic includes genuine tissue motion.
+    It is not an independent deformation model or a certificate of correctness.
+    Reference cloud must come from an independently trusted reference observation.
+    """
+    cols = [f"{a}_um" for a in "xyz"] if physical else XYZ
+    ref = reference_cloud.set_index("track_id")
+    if ref.index.duplicated().any():
+        raise ValueError("Reference identities must be unique")
+    a = tracks[cols].to_numpy(dtype=float)
+    b = ref.reindex(tracks.track_id)[cols].to_numpy(dtype=float)
+    scores = np.linalg.norm(a - b, axis=1)
+    scores[~tracks.validity.to_numpy()] = np.inf
+    # Unseen fragment IDs are unsupported; preserve absence rather than fit a new anatomy.
+    scores[np.isnan(scores)] = np.inf
+    return scores
+
+
+def verifier_metrics(scores, is_error, *, threshold):
+    """Fixed-population selective risk/coverage; ties enter a risk curve together.
+
+    False certification = accepted errors / all labeled errors. Selective risk =
+    accepted errors / accepted observations. Unsupported scores abstain. Threshold
+    must be specified before evaluation; this function does not select it.
+    """
+    scores = np.asarray(scores, dtype=float)
+    labels = np.asarray(is_error, dtype=bool)
+    if len(scores) != len(labels) or not len(scores) or np.isnan(scores).any():
+        raise ValueError("Scores and labels must be aligned, nonempty, and non-NaN")
+    if np.isnan(threshold):
+        raise ValueError("Threshold cannot be NaN")
+    accept = np.isfinite(scores) & (scores <= threshold)
+    # Infinity denotes abstention; map it above all finite scores for ranking metrics.
+    finite = scores[np.isfinite(scores)]
+    ranked = np.where(np.isfinite(scores), scores, (finite.max() + 1 if len(finite) else 1))
+    both = labels.any() and not labels.all()
+    curve = []
+    order = np.flatnonzero(np.isfinite(scores))
+    order = order[np.argsort(scores[order], kind="stable")]
+    if len(order):
+        ordered_scores = scores[order]
+        cumulative_errors = np.cumsum(labels[order])
+        ends = np.r_[np.flatnonzero(np.diff(ordered_scores) != 0), len(order) - 1]
+        curve = [
+            {
+                "threshold": float(ordered_scores[i]),
+                "coverage": float((i + 1) / len(scores)),
+                "risk": float(cumulative_errors[i] / (i + 1)),
+            }
+            for i in ends
+        ]
+    return {
+        "observations": len(scores),
+        "error_prevalence": float(labels.mean()),
+        "auroc": float(roc_auc_score(labels, ranked)) if both else None,
+        "auprc": float(average_precision_score(labels, ranked)) if both else None,
+        "false_certification_rate": float((accept & labels).sum() / labels.sum())
+        if labels.any()
+        else None,
+        "certified_coverage": float(accept.mean()),
+        "selective_risk": float(labels[accept].mean()) if accept.any() else None,
+        "correct_certified_coverage": float((accept & ~labels).mean()),
+        "threshold": float(threshold),
+        "risk_coverage": curve,
+    }
+
+
+def oracle_association_baseline(detections, *, method="hungarian", physical=True):
+    """Untrained Euclidean nearest-neighbor or bipartite association on fixed detections."""
+    if method not in {"nearest_neighbor", "hungarian"}:
+        raise ValueError(method)
+    cols = [f"{a}_um" for a in "xyz"] if physical else XYZ
+    output = []
+    previous = None
+    next_id = 0
+    for frame, points in detections.sort_values("frame_index").groupby("frame_index"):
+        points = points.copy().reset_index(drop=True)
+        xyz = points[cols].to_numpy(dtype=float)
+        if not np.isfinite(xyz).all():
+            raise ValueError("Baseline requires valid coordinates in the selected units")
+        ids = [None] * len(points)
+        if previous is not None:
+            old_xyz, old_ids = previous
+            distance = np.linalg.norm(old_xyz[:, None] - xyz[None], axis=2)
+            if method == "hungarian":
+                rows, columns = linear_sum_assignment(distance)
+            else:
+                pairs = sorted((distance[i, j], i, j) for i, j in enumerate(distance.argmin(1)))
+                chosen = set()
+                matches = []
+                for _, i, j in pairs:
+                    if j not in chosen:
+                        matches.append((i, j))
+                        chosen.add(j)
+                rows = [p[0] for p in matches]
+                columns = [p[1] for p in matches]
+            for i, j in zip(rows, columns, strict=True):
+                ids[j] = old_ids[i]
+        for i, identity in enumerate(ids):
+            if identity is None:
+                ids[i] = f"baseline:{next_id}"
+                next_id += 1
+        points["track_id"] = ids
+        points["annotation_type"] = "algorithm_prediction"
+        points["label_set"] = "prediction"
+        output.append(points)
+        previous = xyz, ids
+    return pd.concat(output, ignore_index=True)