dataset_name stringclasses 3
values | script_name stringclasses 1
value | model stringclasses 1
value | hyperparameters stringclasses 1
value | input_datasets stringclasses 1
value | description stringclasses 3
values | tags stringclasses 2
values | custom_metadata stringclasses 1
value | updated stringclasses 3
values | experiment_id null | run_id null | artifact_type null | visualizer_type null | artifact_group null | parent_artifact null | size_bytes int64 -1 -1 | created stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T0_modeling_experimentation-canary-errors-v1 | t0_modeling/minimal_placebo.py | none (CPU simulation; classical SC estimators) | {"dgp": {"n_donors": 15, "T_pre": 96, "T_post": 32, "n_latents": 4, "latent_mode": "kernelsynth", "loading_family": "simplex", "mixing_mode": "static", "tau_max": 0, "signal_share": 0.9, "noise_model": "per_unit", "share_concentration": 20.0, "noise_floor": 0.001, "shock_ratio": 0.25, "shock_phi": 0.8}, "score": {"gamm... | [] | Per-target, per-horizon prediction errors for the T0 selection placebo (canary, 10 seeds x 2 sigma_z x 5 arms x 2 targets x 14 methods x 26 horizons). | ["synthetic-control", "t0-modeling", "selection", "placebo", "canary"] | {"experiment_name": "T0_modeling_experimentation", "job_id": "local:canary", "cluster": "local (macOS laptop)", "artifact_status": "final", "canary": true, "code_commit": "b595920ef04d96e8151712a32fd4da333f4d3bab", "reproduce": "git checkout b595920ef04d96e8151712a32fd4da333f4d3bab && python -m t0_modeling.minimal_plac... | 2026-09-30T20:14:32.226857+00:00 | null | null | null | null | null | null | -1 | 2026-09-30T20:14:32.226857+00:00 |
T0_modeling_experimentation-canary-units-v1 | t0_modeling/minimal_placebo.py | none (CPU simulation; classical SC estimators) | {"dgp": {"n_donors": 15, "T_pre": 96, "T_post": 32, "n_latents": 4, "latent_mode": "kernelsynth", "loading_family": "simplex", "mixing_mode": "static", "tau_max": 0, "signal_share": 0.9, "noise_model": "per_unit", "share_concentration": 20.0, "noise_floor": 0.001, "shock_ratio": 0.25, "shock_phi": 0.8}, "score": {"gamm... | [] | Per-unit selection diagnostics for the T0 selection placebo (canary): T0, q, peak score, threshold, convex-hull distance of the loadings, shock at T0. | ["synthetic-control", "t0-modeling", "selection", "placebo", "canary"] | {"experiment_name": "T0_modeling_experimentation", "job_id": "local:canary", "cluster": "local (macOS laptop)", "artifact_status": "final", "canary": true, "code_commit": "b595920ef04d96e8151712a32fd4da333f4d3bab", "reproduce": "git checkout b595920ef04d96e8151712a32fd4da333f4d3bab && python -m t0_modeling.minimal_plac... | 2026-09-30T20:14:36.987053+00:00 | null | null | null | null | null | null | -1 | 2026-09-30T20:14:36.987053+00:00 |
T0_modeling_experimentation-canary-config-v1 | t0_modeling/minimal_placebo.py | none (CPU simulation; classical SC estimators) | {"dgp": {"n_donors": 15, "T_pre": 96, "T_post": 32, "n_latents": 4, "latent_mode": "kernelsynth", "loading_family": "simplex", "mixing_mode": "static", "tau_max": 0, "signal_share": 0.9, "noise_model": "per_unit", "share_concentration": 20.0, "noise_floor": 0.001, "shock_ratio": 0.25, "shock_phi": 0.8}, "score": {"gamm... | [] | Everything needed to reproduce the run: full config, commit, runner and score source, and the diff against main. | ["synthetic-control", "t0-modeling", "selection", "placebo", "canary", "config"] | {"experiment_name": "T0_modeling_experimentation", "job_id": "local:canary", "cluster": "local (macOS laptop)", "artifact_status": "final", "canary": true, "code_commit": "b595920ef04d96e8151712a32fd4da333f4d3bab", "reproduce": "git checkout b595920ef04d96e8151712a32fd4da333f4d3bab && python -m t0_modeling.minimal_plac... | 2026-09-30T20:14:41.253896+00:00 | null | null | null | null | null | null | -1 | 2026-09-30T20:14:41.253896+00:00 |
RACA-PROJECT-MANIFEST
Central registry of all datasets in the meganrichards3 organization.
- Total Datasets Tracked: 3
- Last Updated: 2026-09-30T20:14:42.268085+00:00
Usage
from datasets import load_dataset
manifest = load_dataset("meganrichards3/RACA-PROJECT-MANIFEST", split="train")
print(f"Tracking {len(manifest)} datasets")
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