Datasets:

Oracle4CEG
/

OracleEconLab-Oracle-Incentives-v1

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CITATION.cff CHANGED Viewed

@@ -1,10 +1,25 @@
 cff-version: 1.2.0
-message: "If you use this dataset or pipeline, please cite this release."
-type: dataset
-title: "Oracle-Nature Economic Research Release"
-version: 1.5.0
-date-released: 2026-08-06
-license: CC-BY-4.0
 authors:
-  - name: "Oracle-Nature Contributors"
-abstract: "An audit-first cross-protocol dataset of Oracle incentive, penalty, dispute, settlement, and decision-time evidence."

 cff-version: 1.2.0
+message: Cite this data version and its resolved commit. No DOI is assigned.
+type: data
+title: 'Oracle4SD Atlas: eight oracle branches, UMA tasks and daily measurements'
 authors:
+- family-names: Jia
+  given-names: Huaiyu
+  affiliation: The Hong Kong University of Science and Technology
+  website: https://openreview.net/profile?id=~Huaiyu_Jia2
+- family-names: Li
+  given-names: Siguang
+  affiliation: The Hong Kong University of Science and Technology
+  website: https://openreview.net/profile?id=~Siguang_Li1
+- family-names: Cong
+  given-names: Lin William
+  affiliation: Nanyang Technological University
+  website: https://openreview.net/profile?id=~Lin_William_Cong1
+- family-names: Zhang
+  given-names: Luyao
+  affiliation: Duke University
+  website: https://openreview.net/profile?id=~Luyao_Zhang1
+version: atlas-utc-20261029
+year: 2026
+url: https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/tree/atlas-utc-20261029
+license: CC-BY-4.0

README.md CHANGED Viewed

@@ -2,254 +2,122 @@
 license: cc-by-4.0
 language:
 - en
-task_categories:
-- tabular-classification
-pretty_name: OracleEconLab Oracle Incentives and Accountability v1
-size_categories:
-- 100M<n<1B
 tags:
 - blockchain
-- oracle
-- economics
-- trustworthy-ai
-- tabular
-configs:
-- config_name: abc_dictionary
-  data_files: data/abc_dictionary.parquet
-- config_name: uma_real_episode
-  data_files: data/uma_real_episode.parquet
-- config_name: uma_real_episode_abc
-  data_files: data/uma_real_episode_abc.parquet
-- config_name: uma_public_rpc_episode
-  data_files: data/uma_public_rpc_episode.parquet
-- config_name: uma_public_rpc_abc
-  data_files: data/uma_public_rpc_abc.parquet
-- config_name: uma_decisions
-  data_files: data/uma_decisions.parquet
-- config_name: uma_decision_splits
-  data_files: data/uma_decision_splits.parquet
-- config_name: decision_evidence
-  data_files: data/decision_evidence.parquet
-- config_name: ai_feature_dictionary
-  data_files: data/ai_feature_dictionary.parquet
-- config_name: public_ai_episodes
-  data_files: data/public_ai_episodes.parquet
-- config_name: public_ai_decisions
-  data_files: data/public_ai_decisions.parquet
-- config_name: public_ai_splits
-  data_files: data/public_ai_splits.parquet
-- config_name: public_ai_evidence
-  data_files: data/public_ai_evidence.parquet
-- config_name: public_ai_predictions
-  data_files: data/public_ai_predictions.parquet
-- config_name: public_ai_metrics
-  data_files: data/public_ai_metrics.parquet
 ---
-# OracleEconLab Oracle Incentives and Accountability v1
-This review-sized draft turns fixed, public UMA protocol evidence into complete
-or right-censored economic lifecycles. It is a release candidate for the paper
-*Who Verifies Decentralized Information? Economic Incentives, Accountability,
-and Trustworthy AI Across Oracle Protocols.*
-## Dataset configurations
-| Configuration | Rows | Observation unit | Purpose |
-|---|---:|---|---|
-| `abc_dictionary` | 28 | economic variable | Formulas, units, sources, missing rules and A/B/C family |
-| `uma_real_episode` | 1 | cross-chain episode | End-to-end Polygon OOV2 to Ethereum DVM validation |
-| `uma_real_episode_abc` | 28 | case-measure status | Honest mapping of every A/B/C measure to the real case |
-| `uma_public_rpc_episode` | 1 | cross-chain episode | Clean-room episode reconstructed without curated-ledger input |
-| `uma_public_rpc_abc` | 28 | case-measure status | Public-source-only A/B/C mapping and explicit unavailable states |
-| `uma_decisions` | 810 | challenged proposal-time decision | Minimal four-action AI evaluation |
-| `uma_decision_splits` | 810 | decision split assignment | Frozen chronological train/validation/test partition |
-| `decision_evidence` | 2430 | timestamped evidence reference | Prediction-level provenance and leakage audit |
-| `ai_feature_dictionary` | 16 | decision-time feature | Feature meaning, source and availability |
-| `public_ai_episodes` | 64 | public-source cross-chain episode | Economic lifecycle reconstructed from fixed public receipts |
-| `public_ai_decisions` | 64 | proposal-time decision | Credential-free public-source AI demonstration inputs |
-| `public_ai_splits` | 64 | split assignment | Label-free chronological 44/10/10 partition |
-| `public_ai_evidence` | 192 | timestamped evidence reference | Decision-time traceability for every demonstration prediction |
-| `public_ai_predictions` | 70 | model/action output | Four-action predictions, confidence, evidence and protocol endpoint |
-| `public_ai_metrics` | 7 | model evaluation | Calibration, coverage, abstention and stylized economic cost |
-## A/B/C economic measure families
-- **A — Verification concentration (Who verifies?):** HHI, top-k share,
-  effective number of monitors and threshold monitor count.
-- **B — Incentives and frictions (Why verify?):** reward/bond, capital-days,
-  fees, Gas, verification cost and scenario-based expected private utility.
-- **C — Accountability outcomes (What happens?):** dispute, adjudication,
-  transfer, realized payoff, delay and right censoring.
-A/B/C are measure families. They are not the project's Sample A/B/C data
-cohorts. Identity, decision-time, cross-chain-link and provenance fields remain
-supporting fields rather than being mislabeled as economic outcomes.
-## Construction and provenance
-The fixed cutoff is `2026-06-30T23:59:59Z`. The real case begins with canonical
-Polygon request, proposal, dispute and settlement records, links them to the
-Ethereum DVM with a Grade-A deterministic match, reconciles token flows, and
-separates returned principal from realized reward. Every processed episode or
-decision contains transaction/evidence references and a deterministic
-provenance identifier. File integrity is recorded in `checksums.csv`.
-The main reconstruction commands live in the GitHub `real-release-v1.5`
-branch. `public_evidence/` includes the five fixed receipts, canonical headers,
-case specification, source checksums and verified contract-semantic rules used
-by the clean-room entry. This draft contains review-sized processed evidence; it does not claim
-that every protocol ledger can be regenerated without archive infrastructure.
-The executed `notebooks/uma_minimal_trustworthy_ai_demo.ipynb` loads the frozen
-tables, reruns the leakage audit and chronological evaluation, and writes the
-review-sized artifacts under `demo_outputs/`. Its reported performance is not
-edited or filtered after execution.
-The independent `public_ai_evidence/` path closes the public-source chain for a
-fixed 64-episode demonstration: a label-free transaction registry, 280 canonical
-Polygon/Ethereum receipt and header bundles, decoded economic episodes,
-decision-time features, model outputs and QC. Live mode uses credential-free
-public endpoints; offline mode deterministically replays the frozen snapshot.
-The model result is intentionally reported even when weak. It is a pipeline
-demonstration rather than evidence of production challenge quality.
-## Trustworthy-AI task
-At the OOV2 proposal timestamp, the benchmark emits one of `Accept`,
-`Investigate`, `Challenge`, or `Abstain`. The endpoint is whether the later UMA
-protocol resolution rejects the proposal. Only decision-time fields may be
-used as model inputs. Columns whose names end in `_outcome_only`, the protocol
-label, dispute result and settlement evidence are evaluation-only and must not
-enter the model.
-The 810 observations condition on proposals that were actually challenged,
-Grade-A linked, price-consistent and flow-exact. The task is therefore a
-conditional protocol-outcome evaluation, not a causal policy for all proposals
-and not an independent truth classifier.
-## Intended uses
-- audit economic variable construction and source traceability;
-- reproduce the real UMA episode and its payoff decomposition;
-- evaluate calibration, abstention, leakage and robustness on the conditional
-  decision cohort;
-- study A/B/C constructs within their declared observation units.
-## Uses that are not validated
-- production deployment of automated challenges;
-- causal claims that rewards or penalties improve truthfulness;
-- treating protocol acceptance or DVM resolution as independent factual truth;
-- identity, geography or intent inference from blockchain addresses;
-- aggregation of heterogeneous token amounts without asset-preserving conversion.
-## Biases and limitations
-The decision cohort has selective labels because it includes only actually
-challenged UMA requests. It over-represents mechanisms and market adapters for
-which exact cross-chain and token-flow evidence is recoverable. Address-level
-concentration is not entity-level concentration. Off-chain investigation labor
-is unavailable and remains a declared scenario. Independent factual truth is
-available only for a small external-rule subset outside the core protocol label.
-## Personal and sensitive information
-The data contain public pseudonymous contract/account addresses, transaction
-hashes and block metadata. They contain no asserted mapping from an address to
-a natural person and must not be used to infer human identity. RPC endpoint
-URLs, credentials and private keys are not included.
-## Licensing
-Original curated tables and documentation are CC BY 4.0. Upstream contracts,
-APIs and third-party records remain governed by their own terms. See
-`DATA_LICENSE.md` and `CITATION.cff`.
-## Pinned research delivery added 10 October 2026
-The existing root-level `data/` and public-evidence directories remain the previously
-released demonstration resource. Two separately named archives now support the
-revised agentic-audit research:
-- `atlas/selected-inputs/2026-10-10/`: four fixed curated UMA source tables used by
-  research cohort, feature and label reconstruction. This is a **selected Atlas
-  snapshot**, not the complete 67-file candidate Atlas or a claim that its known
-  semantic problems are resolved.
-- `research/agentic-audit/2026-10-10/`: experiment-specific 810-case cohort and
-  chronological splits, frozen evidence, original Qwen response records, additional
-  experiments, archived failed/interrupted/superseded attempts, 73 reference CSVs,
-  18 numeric LaTeX tables, exact prompts and eight original figures. Historical
-  GPT records are separate provenance and are excluded from Qwen comparisons.
-The active supplemental scope is 483 validation runs, 320 matched B2 runs (repeats
-3 and 4), and 480 A1 guard runs. Optional low-temperature experiments were cancelled.
-Original Qwen main and corruption panels contain 1,600 and 320 case-runs. Raw model
-outputs are machine-generated records, not endorsements of their contents or
-independent factual truth. Read each manifest and the research limitations.
-Research code: https://github.com/Oracle4CEG/Oracle-Agentic-Audit (delivery through a
-personal-fork pull request). Atlas construction: https://github.com/Oracle4CEG/Oracle4SD-Atlas.
-The research `manifests/public-release.json` pins the exact dataset commit and SHA-256
-of each archive. `python -m experiments.download` downloads anonymously and verifies
-all files; `python -m analysis.replay --retrain` refits CPU baselines and replays saved
-LLM records without a GPU or inference service. Hosted Colab acceptance remains a
-separate check; no hosted execution is claimed by uploading this dataset.
-No model weights, credentials or confidential manuscripts are included. The existing
-data license applies to original curated data; archived source/code and third-party
-content retain their original terms. Research code now has author-confirmed MIT terms. The archive does not certify
-a public upstream model checkpoint revision.
-### Recorded research acceptance and code license
-The public inputs remain pinned to dataset commit
-`ee197fc6fd6b8d980e7c7dda4feae966efe5d28c`. The acceptance directory
-`research/agentic-audit/2026-10-10/acceptance/github-notebook/` contains the actually
-executed notebook, environment, anonymous-download receipt, replay receipt and
-per-table comparison. All six code cells completed in a fresh GitHub Actions environment;
-73 scientific CSV comparisons and 18 numeric LaTeX tables plus exact prompts passed,
-and eight figures were regenerated. The receipt records elapsed time and figure
-hash equality separately. Separate delivery notes record the earlier local network interruptions.
-This is a **GitHub Actions fresh-notebook acceptance**, not a hosted Google Colab run.
-GitHub Actions independently passed the recorded CPU replay. No new LLM inference
-was performed. The commit-pinned notebook is available in research PR #2; final
-hosted Colab and independent scientific review remain pending.
-The author confirmed MIT for original research code on 10 October 2026. See the
-research repository's LICENSE and DATA_LICENSE.md. Dataset and third-party terms
-are unchanged. This acceptance addition does not change the bytes or identities
-of the previously published Atlas-input and research-record archives.
-## Atlas reconstruction inputs — 12 October 2026
-The original 15 viewer tables above remain a small review subset. Full Atlas offline reconstruction now has a separate frozen component: 64 native/curated input Parquet files, 1,807,346,651 bytes. [Download the native snapshot](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/tree/c7df3127756d831a61c781d0531c155dc8d9c2fb/atlas/releases/atlas-repaired-20261012/native). Five pinned public source/registry archives are also retained separately.
-[Executable code and commands](https://github.com/virusLuke3/Oracle4SD-Atlas/tree/fix/usable-atlas-release-20261012) verify file hashes before reconstructing common, economic and application layers. The source is a repair branch; execution reports specify validated scope. Historical source snapshots are retained, not silently relabeled as corrected outputs.
-The root Croissant file describes only the original 15 review tables. Its JSON-LD, Boolean types, immutable file links and RAI descriptions were repaired and all 15 record sets were loaded with mlcroissant 1.1.0. See review_metadata_validation.json and review_provenance.json. These checks do not establish complete historical person/agent provenance or full-Atlas coverage. This revision has no dataset DOI.
-## Validated full Atlas outputs — 12 October 2026
-[Download the repaired Atlas](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/tree/57ab1e2409652291c75f02c9e066c52011d8b4ad/atlas/releases/atlas-repaired-20261012/repaired): **95 canonical Parquet tables, 17,341,941,711 Parquet bytes**, plus one decision-prediction Parquet and execution/metadata files. The common layer has 105,588,120 records. Layers overlap and must not be added as independent observations. Data revision: `57ab1e2409652291c75f02c9e066c52011d8b4ad`. Scientific code: `6317a1b66bc6f9653b21650a7d170dc762891a31`; the preserved lineage records the earlier native build and explicit repairs.
-The reconstruction passes **50 release checks**; 27 regression tests and all nine local notebook cells also pass. Seven financial models complete 21 fits over three tasks; three decision baselines use 810 frozen observations. Financial F1/F2 use an explicitly marked random split fallback, F3 is chronological, and unavailable metrics remain null. High pooled scores are not evidence of cross-protocol generalization, independent factual truth or profitable decisions.
-The release includes [table/field documentation](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/tree/57ab1e2409652291c75f02c9e066c52011d8b4ad/atlas/releases/atlas-repaired-20261012/repaired/metadata), [source and transformation provenance](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/tree/57ab1e2409652291c75f02c9e066c52011d8b4ad/atlas/releases/atlas-repaired-20261012/repaired/metadata/revision_provenance.json), before/after semantic checks, timings, memory usage and full logs. All 1,789 fields have type/definition coverage; native source-reference descriptions do not establish complete historical collection or person/agent provenance. Missing timestamps and censoring remain explicit. The old 71-file inventory and its audit failures are retained separately.
-For installation, selected downloads and full reconstruction, use the [Chinese quick start](https://github.com/virusLuke3/Oracle4SD-Atlas/blob/fix/usable-atlas-release-20261012/docs/QUICKSTART_ZH.md) or [full guide](https://github.com/virusLuke3/Oracle4SD-Atlas/blob/fix/usable-atlas-release-20261012/docs/FULL_DATA.md). The downloader verifies immutable URLs, sizes and SHA-256. Frozen replay requires no wallet, paid RPC or inference service.
-The root viewer configurations and root Croissant still cover the original 15 review tables; full Atlas tables use the separately linked catalog and manifests. Archived source-registry trees preserve their upstream licenses and are not relicensed by the Dataset Card's CC BY designation. No hosted Google Colab pass, independent scientific approval or dataset DOI is claimed.
-## Atlas authors and acquisition supplement — 12 October 2026
-For the Atlas release, the requester supplied this author order: **Huaiyu Jia; Siguang Li; Lin William Cong; Luyao Zhang**. Jia and Li: The Hong Kong University of Science and Technology (hkust-gz.edu.cn); Cong: Nanyang Technological University (ntu.edu.sg); Zhang: Duke University (duke.edu). These affiliations are retained as supplied; no ORCIDs were inferred. This statement applies to the Atlas release, not every other study hosted in this repository.
-[Download the versioned code and acquisition supplement](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/tree/757c8e3e17e6c302423252f493fc0fc4db498fe3/atlas/releases/atlas-repaired-20261012/acquisition-supplement), archived from code commit `38e63d2ed503b38b97d2fddd484f7871b9dda7de`. It includes all 64 input-table provenance bindings, 39 retained historical manifests, 52 available producer identities and a fresh 688-file raw hash audit. It records conservative build-level lineage for all 95 canonical outputs. One previously mismatched historical manifest has been recovered from a fixed public revision and matched to its expected digest. Historical collection operators and executed collector commits remain unknown where original receipts are missing.
-The current delivery passes 35 local regression tests; the pinned tutorial execution commit runs its original 28 tests. [Colab acceptance instructions](https://github.com/virusLuke3/Oracle4SD-Atlas/blob/38e63d2ed503b38b97d2fddd484f7871b9dda7de/docs/COLAB_ACCEPTANCE_ZH.md) require an executed notebook, evidence ZIP and operator statement. Local execution is not a hosted Colab pass. The four-author DOI registration metadata is prepared, but no DOI has been assigned.

 license: cc-by-4.0
 language:
 - en
+configs:
+- config_name: uma
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/polygon_uma_request_rounds.parquet
+- config_name: chainlink
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/chainlink_staking_v02_events.parquet
+- config_name: tellor
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/tellor_disputes.parquet
+- config_name: flare_ftsov2
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/flare_reward_epochs.parquet
+- config_name: pyth
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/pyth_ois_reward_epochs.parquet
+- config_name: chronicle
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/chronicle_ethereum_events.parquet
+- config_name: redstone_push
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/redstone_ethereum_push_events.parquet
+- config_name: dia
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/dia_staking_withdrawals.parquet
+- config_name: uma_prediction
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/prediction/uma_prediction_tasks.parquet
+- config_name: daily_measurements
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/daily/daily_measurements.parquet
+pretty_name: 'Oracle4SD Atlas: eight oracle branches, UMA tasks and daily measurements'
 tags:
 - blockchain
+- oracles
+- economic-measurement
+- time-series
 ---
+# Oracle4SD Atlas
+This release contains ten logical data products for studying oracle participation, incentives and accountability. Eight products retain protocol-native observations; the other two provide UMA request-level prediction tasks and daily measurements. The full release has **100 catalogued analysis tables**. Tables shared across products are counted once in that inventory. Additional Parquet files hold supporting metadata, mirrored nested records or retained reproduction inputs; they are listed separately in the package inventory.
+**Version:** `atlas-utc-20261029`. [Processing code](https://github.com/Oracle4CEG/Oracle4SD-Atlas/tree/71c0e19d3ae2b47d763243371f1bfb97750db61d) · [Manuscript](https://github.com/Oracle4CEG/Oracle4SD-Atlas/blob/9d7a227d4895f31a5c2b865d4dd66e3d1ce88b8a/paper/working_revision/main.pdf), [full table catalogue](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/blob/main/atlas/releases/atlas-utc-20261029/metadata/table_catalog.json), [field dictionary](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/blob/main/atlas/releases/atlas-utc-20261029/metadata/field_dictionary.csv), [product membership](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/blob/main/atlas/releases/atlas-utc-20261029/metadata/logical_products.json).
+## Ten datasets
+Each Viewer configuration opens one representative table with a consistent schema. The table count below includes all native and shared tables associated with that product; it is not the number of tables loaded by the Viewer. Use the product manifest to retrieve additional tables. A `train` split is only a loading label here and does not define a modelling partition.
+| Configuration | Product | Preview rows | Associated tables |
+| --- | --- | ---: | ---: |
+| `uma` | UMA | 42,676 | 42 |
+| `chainlink` | Chainlink | 174,890 | 21 |
+| `tellor` | Tellor | 13 | 32 |
+| `flare_ftsov2` | Flare FTSOv2 | 183 | 29 |
+| `pyth` | Pyth | 49 | 24 |
+| `chronicle` | Chronicle | 140,526 | 19 |
+| `redstone_push` | RedStone Push | 614,319 | 18 |
+| `dia` | DIA | 129 | 19 |
+| `uma_prediction` | UMA prediction | 810 | 3 |
+| `daily_measurements` | Daily measurements | 13,269 | 12 |
+UMA, Chainlink, Tellor, Flare FTSOv2 and Pyth are the detailed panels. Chronicle, RedStone Push and DIA are narrower extensions. The Push sample does not cover all RedStone services or AVS economics. Coverage varies by implementation, chain, period and source; first and last observations do not establish uninterrupted collection.
+## Loading data
+```python
+from datasets import load_dataset
+dataset = load_dataset(
+    "Oracle4CEG/OracleEconLab-Oracle-Incentives-v1",
+    "daily_measurements",
+    revision="atlas-utc-20261029",
+    split="train",
+)
+```
+For tables beyond a configuration's preview, download the declared file directly. This avoids combining tables that have different observation units or schemas.
+```python
+from huggingface_hub import hf_hub_download
+import pandas as pd
+path = hf_hub_download(
+    repo_id="Oracle4CEG/OracleEconLab-Oracle-Incentives-v1", repo_type="dataset",
+    revision="atlas-utc-20261029",
+    filename="atlas/releases/atlas-utc-20261029/data/prediction/feature_availability.parquet",
+)
+availability = pd.read_parquet(path)
+```
+The repository's `scripts/load_atlas_product.py` lists product members, downloads selected tables and verifies their SHA-256 hashes. Downloading every physical table requires about 25.5 GB, plus space for caches or analysis outputs.
+## Measurement and reuse
+Daily measurements contain **13,269 rows** grouped by UTC date, protocol branch, mechanism, role, asset and economic stage. They retain both the source population and the subset with supported times. All rows carry a partial-coverage qualification. Missing days are not filled with zero. Escrow exposure covers observed closed requests; signing weights describe epoch policies and are not deposited capital.
+The UMA task contains **810 requests and 16 candidate features**. Its cohort is retrospectively restricted to challenged, linked, settled and transfer-reconciled requests. The label compares the protocol's proposed and resolved integers, including special resolution values; it is not independent factual truth. Four candidate features—bond, configured reward, reward/bond ratio and adapter version—still require historical evidence that binds their values to proposal time. Exclude them from a claim of verified decision-time prediction until that evidence is supplied. Feature-level availability and missingness are published with the task. No model-performance claim is made for this release.
+Native tables preserve source fields and add supported UTC clocks. Common tables retain their earlier fields; join their clock companions by the declared record identifier before selecting time windows. Epoch intervals, snapshots, accruals, claims, payments and applied losses have different meanings. Shared core tables contain multiple branches and overlapping representations: filter the intended branch and choose one observation layer before aggregation.
+Automated checks cover file identity, physical schemas, temporal transformations and specified accounting rules. Human review of historical bindings and an independent reader's Google-hosted Colab run remain pending. Historical acquisition receipts, some block times and the complete cross-chain relay are incomplete. Addresses do not establish independent organizations. See the evidence records for each limitation rather than treating unknown values as zero.
+## Versions and citation
+The previous 15-config experimental release remains available at [its fixed revision](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/tree/356fb1aeac18fb4e6672ca1ea56988dac72c3417). Its files are retained. This release changes the default Viewer to ten products and adds a separate version directory; it does not relabel earlier model outputs as newly validated data.
+Cite: Oracle4CEG. *Oracle4SD Atlas: eight oracle branches, UMA tasks and daily measurements*. Version `atlas-utc-20261029`. Hugging Face, 2026. Record the resolved commit hash with each analysis. No DOI has been assigned to this release.
+Project-created data and metadata use CC BY 4.0. Third-party source rights remain as recorded in [DATA_LICENSE.md](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/blob/main/atlas/releases/atlas-utc-20261029/DATA_LICENSE.md). Code licensing is specified in the code repository.

atlas/releases/atlas-utc-20261029/CITATION.cff ADDED Viewed

	@@ -0,0 +1,25 @@

+cff-version: 1.2.0
+message: Cite this data version and its resolved commit. No DOI is assigned.
+type: data
+title: 'Oracle4SD Atlas: eight oracle branches, UMA tasks and daily measurements'
+authors:
+- family-names: Jia
+  given-names: Huaiyu
+  affiliation: The Hong Kong University of Science and Technology
+  website: https://openreview.net/profile?id=~Huaiyu_Jia2
+- family-names: Li
+  given-names: Siguang
+  affiliation: The Hong Kong University of Science and Technology
+  website: https://openreview.net/profile?id=~Siguang_Li1
+- family-names: Cong
+  given-names: Lin William
+  affiliation: Nanyang Technological University
+  website: https://openreview.net/profile?id=~Lin_William_Cong1
+- family-names: Zhang
+  given-names: Luyao
+  affiliation: Duke University
+  website: https://openreview.net/profile?id=~Luyao_Zhang1
+version: atlas-utc-20261029
+year: 2026
+url: https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/tree/atlas-utc-20261029
+license: CC-BY-4.0

atlas/releases/atlas-utc-20261029/COLAB_ACCEPTANCE.md ADDED Viewed

	@@ -0,0 +1,11 @@

+# Independent-reader and Google Colab acceptance
+Open [the publication notebook](https://colab.research.google.com/github/Oracle4CEG/Oracle4SD-Atlas/blob/9d7a227d4895f31a5c2b865d4dd66e3d1ce88b8a/notebooks/Oracle4SD_Atlas_Tutorial.ipynb) in a Google-hosted Python CPU runtime. Disconnect and delete any existing runtime, reconnect, and run every cell in order. Do not connect a local runtime or edit assertions. The notebook allocates a fresh temporary directory and bootstraps the scientific environment. Archive replay needs no API credential.
+Expected outputs are one linked UMA case, eight bounded native-sample checks, a 100-table catalogue, a 13,269-row daily-table inspection, 28 passing case checks and an unchanged scientific summary on repeat. Native UTC fields retain nulls. Public-download and live examples are optional and use separate directories; preserve a real access error rather than replacing it with an empty result.
+Download the executed notebook with all outputs and the final `oracle-atlas-evidence.zip` before deleting the runtime. The displayed inventory identifies case inputs, results, native sample checks and environment logs. This ZIP is an execution receipt, not the full dataset. The code commit and data revision are recorded separately.
+Supply the actual operator name, date/time zone, hosted CPU runtime, whether the runtime was fresh, every manual intervention and any unresolved difference. A second reader should repeat this process from the public links without using the author's outputs. Do not treat a runtime label alone as proof of server ownership. Do not enter a fictitious reviewer or approval.
+If setup fails, retain the command/bootstrap log. If a checksum or assertion fails, preserve the input revision and failure; never change the expected value to force success. Restart and Run all after an interruption. File an issue in Oracle4SD-Atlas with the receipt and concise steps, excluding credentials. A successful local run is retained in the repository; actual independent-reader and hosted acceptance remain pending.

atlas/releases/atlas-utc-20261029/DATA_LICENSE.md ADDED Viewed

	@@ -0,0 +1,16 @@

+# Dataset and documentation license
+SPDX-License-Identifier: CC-BY-4.0
+Copyright © 2026 Oracle-Nature contributors.
+Unless a file states otherwise, original curated datasets, variable
+dictionaries, reports, figures, and documentation in this release are made
+available under the Creative Commons Attribution 4.0 International license:
+https://creativecommons.org/licenses/by/4.0/legalcode
+Attribution should cite the release using `CITATION.cff`, identify the release
+version, and indicate whether changes were made. This license applies only to
+original contributions and does not replace upstream terms governing source
+APIs, contract source code, archived web pages, or third-party records.

atlas/releases/atlas-utc-20261029/Oracle4SD_Atlas.pdf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:3c5dbe7897a22a8db10b0b8cdc878b16e6ce36b5928a64df830999a870078eba
+size 793122

atlas/releases/atlas-utc-20261029/Oracle4SD_Atlas_Tutorial.ipynb ADDED Viewed

	@@ -0,0 +1,357 @@

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2a68303d",
+   "metadata": {},
+   "source": [
+    "# Oracle4SD Atlas: from native evidence to economic measurements\n",
+    "\n",
+    "This tutorial follows the Template's eight stages. It reconstructs one UMA settlement, checks bounded native samples for eight oracle branches, and inspects the daily measurement product. It does not collect every historical record. Each section states its observation unit and output.\n",
+    "\n",
+    "| Section | Output | Main code |\n",
+    "|---|---|---|\n",
+    "| [1. Setup](#1-setup) | Version and environment record | `scripts/bootstrap_environment.py` |\n",
+    "| [2. Sources](#2-sources-and-metadata) | Source, scope and rights tables | `metadata/acquisition_profiles.json` |\n",
+    "| [3. Query](#3-querying) | Raw responses and acquisition receipts | `code/query_data/query.py`, `tutorial_revision/query.py` |\n",
+    "| [4. Inspect raw data](#4-queried-data-inspection) | Receipt and event locators | case source specifications |\n",
+    "| [5. Process](#5-processing) | One linked episode and eight sample checks | `code/process_data/process.py` |\n",
+    "| [6. Inspect tables](#6-processed-data-inspection) | Dictionaries, keys and physical inventory | `metadata/revision/table_catalog.json` |\n",
+    "| [7. Describe](#7-descriptive-analysis) | Accounting, missingness and daily summaries | `code/analyze_data/analyze.py` |\n",
+    "| [8. Validate and export](#8-validation-and-reproduction) | Check matrix and evidence ZIP | `code/technical_validation/validate.py` |\n",
+    "\n",
+    "An oracle provides claims to a blockchain program. A receipt records transaction execution; its logs record contract events. A reward is distinct from a return of the participant's principal. Protocol resolution does not independently verify an external fact.\n",
+    "\n",
+    "## 1. Setup\n",
+    "\n",
+    "Use a CPU runtime. The case uses a managed Python 3.12 environment; no key or wallet is needed for archived replay. The small native samples are bundled. Full-table downloads are separate. See the English acceptance guide in `docs/COLAB_ACCEPTANCE.md` for restart, export and independent-reader instructions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0eb17b11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import csv, hashlib, json, os, platform, shutil, subprocess, sys, tempfile, time\n",
+    "from IPython.display import display, Markdown, FileLink\n",
+    "CODE_REF = \"71c0e19d3ae2b47d763243371f1bfb97750db61d\"\n",
+    "WORK = Path(tempfile.mkdtemp(prefix=\"oracle-atlas-\"))\n",
+    "def command(args, cwd=None):\n",
+    "    env=dict(os.environ, MPLBACKEND=\"Agg\")\n",
+    "    result=subprocess.run([str(a) for a in args],cwd=cwd,env=env,capture_output=True,text=True)\n",
+    "    with (WORK/\"commands.log\").open(\"a\") as f:\n",
+    "        f.write(json.dumps([str(a) for a in args])+\"\\n\"+result.stdout+result.stderr+\"\\n\")\n",
+    "    if result.returncode:raise RuntimeError(result.stdout+result.stderr)\n",
+    "    return result.stdout\n",
+    "search=[Path.cwd(),Path.cwd().parent]\n",
+    "REPO=next((p for p in search if (p/\"configs/atlas_utc_release.json\").exists()),None)\n",
+    "if REPO is None:\n",
+    "    REPO=WORK/\"repository\"\n",
+    "    command([\"git\",\"clone\",\"--quiet\",\"https://github.com/Oracle4CEG/Oracle4SD-Atlas.git\",REPO])\n",
+    "    command([\"git\",\"checkout\",\"--detach\",CODE_REF],REPO)\n",
+    "PYTHON=os.environ.get(\"ORACLE_NOTEBOOK_PYTHON\")\n",
+    "if not PYTHON:\n",
+    "    command([sys.executable,REPO/\"scripts/bootstrap_environment.py\",\"--output\",WORK/\"environment\"])\n",
+    "    PYTHON=str(WORK/\"environment/scientific/bin/python\")\n",
+    "import pandas as pd\n",
+    "def read_json(p):return json.loads(Path(p).read_text())\n",
+    "RUN=WORK/\"case\";RUN.mkdir()\n",
+    "NATIVE=WORK/\"native\"\n",
+    "shutil.copytree(REPO/\"tutorial_revision\",NATIVE,ignore=shutil.ignore_patterns(\"*.ipynb\",\"__pycache__\"))\n",
+    "sys.path.insert(0,str(NATIVE));sys.path.insert(0,str(NATIVE/\"processing_code\"))\n",
+    "from query import query\n",
+    "from replay import run_branch\n",
+    "profiles=read_json(NATIVE/\"metadata/profiles.json\")\n",
+    "raw_profiles=read_json(NATIVE/\"metadata/raw_profiles.json\")\n",
+    "def stage(folder, script, extra=(), run=RUN):\n",
+    "    return command([PYTHON,REPO/\"code\"/folder/script,\"--config\",REPO/\"configs/project.json\",\"--run-dir\",run,*extra],REPO)\n",
+    "identity={\"code_commit\":command([\"git\",\"rev-parse\",\"HEAD\"],REPO).strip(),\n",
+    "          \"working_tree_modified\":bool(command([\"git\",\"status\",\"--porcelain\"],REPO).strip()),\n",
+    "          \"data_release\":read_json(REPO/\"configs/atlas_utc_release.json\"),\n",
+    "          \"runtime_kind\":\"hosted_colab\" if \"google.colab\" in sys.modules else \"local_jupyter_kernel\",\n",
+    "          \"host_python\":platform.python_version(),\"independent_reader\":False}\n",
+    "display(identity)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcbad880",
+   "metadata": {},
+   "source": [
+    "## 2. Sources and metadata\n",
+    "\n",
+    "The first register describes seven case files covering five transactions. The historical full-source register has 64 native tables and six archive metadata entries. Native tables are retained collector outputs, not a complete archive of original RPC responses. The current inventory separately describes 100 physical tables.\n",
+    "\n",
+    "The eight branches have different scopes. UMA, Chainlink, Tellor, Flare FTSOv2 and Pyth are detailed panels; Chronicle, RedStone Push and DIA are narrower extensions. The acquisition profiles state chains, implementations, range endpoints, source methods and limitations. Project evidence is distributed under CC BY 4.0; upstream rights are retained in `DATA_LICENSE.md`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ce83a25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(pd.read_csv(REPO/\"metadata/source_register.csv\"))\n",
+    "full_sources=pd.read_csv(REPO/\"metadata/full_source_register_20261012.csv\")\n",
+    "print(\"Historical source register:\",len(full_sources),\"entries\")\n",
+    "display(full_sources.head(4))\n",
+    "scope=pd.DataFrame(read_json(REPO/\"metadata/acquisition_profiles.json\")[\"profiles\"])\n",
+    "display(scope[[c for c in [\"protocol\",\"chain\",\"delivery_chain\",\"contracts\",\"start\",\"end\",\"limitations\"] if c in scope]])\n",
+    "display(pd.DataFrame(profiles)[[\"branch\",\"parent_native_table\",\"source_mode\",\"sample_rows\",\"acquisition_scope\"]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b374b4f",
+   "metadata": {},
+   "source": [
+    "## 3. Querying\n",
+    "\n",
+    "| Mode | What is obtained | Boundary |\n",
+    "|---|---|---|\n",
+    "| Bundled case | Seven registered files | No fresh API requests |\n",
+    "| Public download | Two pinned receipt snapshots; five bundled support files | Secondary archive download |\n",
+    "| Live case | Five receipts, chain identity, headers and retained code | Fixed case; may encounter access failures |\n",
+    "| Eight-branch replay | One registered raw response and a native subset per branch | Bounded identity checks |\n",
+    "\n",
+    "Archived replay is the default. Set `RUN_NETWORK_EXAMPLES=True` to run the separate public-download and live cases. Live responses must pass locator and chain checks; they need not have the same response-envelope bytes as an archive. The eight-branch commands in the acquisition guide each stop after one registered transaction or height, use a 25-second timeout, and retain an error on failure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24cdb9a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stage(\"query_data\",\"query.py\",[\"--mode\",\"bundled\"])\n",
+    "acquisition=read_json(RUN/\"reports/acquisition.json\")\n",
+    "assert acquisition[\"source_transactions\"]==5\n",
+    "assert all(f[\"frozen_hash_matches\"] for f in acquisition[\"files\"])\n",
+    "raw_checks=[query(NATIVE,p[\"branch\"],\"archived_raw\") for p in raw_profiles]\n",
+    "display(pd.DataFrame(raw_checks)[[\"branch\",\"mode\",\"expected_native_block\",\"locator_verified\"]])\n",
+    "RUN_NETWORK_EXAMPLES=False\n",
+    "if RUN_NETWORK_EXAMPLES:\n",
+    "    for mode in [\"public-download\",\"live\"]:\n",
+    "        destination=WORK/mode;destination.mkdir()\n",
+    "        try:\n",
+    "            stage(\"query_data\",\"query.py\",[\"--mode\",mode],destination)\n",
+    "            report=read_json(destination/\"reports/acquisition.json\")\n",
+    "            if mode==\"public-download\":assert all(f[\"frozen_hash_matches\"] for f in report[\"files\"])\n",
+    "            stage(\"process_data\",\"process.py\",run=destination)\n",
+    "            stage(\"technical_validation\",\"validate.py\",run=destination)\n",
+    "            display(report)\n",
+    "        except RuntimeError as error:\n",
+    "            (destination/\"failure.txt\").write_text(str(error))\n",
+    "            print(mode,\"failed; retained diagnostic, no empty replacement\")\n",
+    "else:\n",
+    "    print(\"Network examples not executed in this run. Retained bounded query evidence:\")\n",
+    "    display(pd.DataFrame(read_json(REPO/\"reports/publication_20261029/bounded_acquisition.json\"))[[\"branch\",\"mode\",\"status\",\"elapsed_seconds\"]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6762c53",
+   "metadata": {},
+   "source": [
+    "## 4. Queried-data inspection\n",
+    "\n",
+    "`transactionHash` locates the transaction; `blockHash` identifies the block; `logIndex` locates an emitted event within its chain log order. The raw integer block number is hexadecimal in an Ethereum JSON-RPC receipt. A decoded event is shown after processing, with the source transaction retained. Neither receipt success nor a matching locator proves the external claim was true."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a9bfeda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snapshot=read_json(RUN/\"queried_data/uma_case/raw/polygon_rpc_snapshot.json\")\n",
+    "receipt=next(iter(snapshot[\"receipts\"].values()))\n",
+    "preview={k:receipt.get(k) for k in [\"transactionHash\",\"blockHash\",\"blockNumber\",\"status\"]}\n",
+    "preview[\"first_log\"]={k:receipt[\"logs\"][0].get(k) for k in [\"address\",\"logIndex\",\"topics\",\"data\"]}\n",
+    "display(preview)\n",
+    "display(Markdown(\"[Original transaction](https://polygonscan.com/tx/\"+receipt[\"transactionHash\"]+\")\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "508ca8a9",
+   "metadata": {},
+   "source": [
+    "## 5. Processing\n",
+    "\n",
+    "| Transformation | Input \u2192 output unit | Key or rule |\n",
+    "|---|---|---|\n",
+    "| ABI decoding | Receipt logs \u2192 9 selected events | Chain, transaction and log index |\n",
+    "| Token-flow selection | Transfer logs \u2192 8 USDC.e movements | Token contract and source locator |\n",
+    "| Lifecycle linkage | 5 transactions \u2192 1 episode | OOv2 request and DVM identifier |\n",
+    "| Settlement accounting | Gross payment \u2192 principal and gain | Integer token units; principal is separated |\n",
+    "| Native sample replay | Bounded table \u2192 clock and identity check | Registered SHA-256 and row count |\n",
+    "\n",
+    "The eight sample checks verify retained native inputs; they do not derive the full native history from the single raw response. That boundary is deliberate and appears in every sample profile."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7d1a382",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stage(\"process_data\",\"process.py\")\n",
+    "processing=read_json(RUN/\"reports/processing.json\")\n",
+    "display(processing[\"counts\"])\n",
+    "case_events=pd.read_csv(RUN/\"processed_data/source_events.csv\")\n",
+    "display(case_events.head(1))\n",
+    "sample_checks=[]\n",
+    "for p in profiles:\n",
+    "    checked=run_branch(NATIVE,p[\"branch\"]);sample_checks.append(checked)\n",
+    "    sample=pd.read_parquet(NATIVE/p[\"input\"])\n",
+    "    cols=[c for c in [\"event_time_utc\",\"epoch_start_utc\",\"epoch_end_utc\",\"event_time_status\"] if c in sample]\n",
+    "    print(p[\"branch\"],\"\u2014\",p[\"parent_native_table\"],\"\u2014\",p[\"sample_rows\"],\"retained rows\")\n",
+    "    display(sample[cols].head(2))\n",
+    "display(pd.DataFrame(sample_checks))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "195dc900",
+   "metadata": {},
+   "source": [
+    "## 6. Processed-data inspection\n",
+    "\n",
+    "The episode dictionary defines all 85 fields. Case child tables describe events, transfers, accounting components and checks for the same episode; their row counts are not additional lifecycles. The single-case join is the retained request/episode identity. Source-event and transfer identities also include chain, transaction and log position.\n",
+    "\n",
+    "The current full catalogue contains 100 physical tables, replacing the previous 95-table inventory for this release. Shared layers overlap. Clock companions join their parent records by record identifier; filter the parent branch before aggregation. `event_time_utc` is an observed execution time, epoch bounds are interval support, and `event_time_status` states the basis or absence of an event clock. Missing times remain null. Dictionary definition status distinguishes complete revision rules from native fields requiring further semantic review."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e08a5e69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "episode=read_json(RUN/\"processed_data/economic_episode.json\")\n",
+    "dictionary=pd.read_csv(REPO/\"metadata/data_dictionary.csv\")\n",
+    "assert set(dictionary.field_name)==set(episode)\n",
+    "display(dictionary[[\"field_name\",\"type_or_enum\",\"unit\",\"missing_policy\"]].head(8))\n",
+    "case_tables=read_json(REPO/\"metadata/table_dictionary.json\")[\"tables\"]\n",
+    "display(pd.DataFrame.from_dict(case_tables,orient=\"index\"))\n",
+    "print(\"The tenth case artifact is economic_episode.json: the same episode as economic_episodes.csv, not an additional observation.\")\n",
+    "catalog=pd.DataFrame(read_json(REPO/\"metadata/revision/table_catalog.json\"))\n",
+    "assert len(catalog)==100\n",
+    "display(catalog[[\"path\",\"products\",\"layer\",\"observation_unit\",\"rows\"]])\n",
+    "full_dictionary=pd.read_csv(REPO/\"metadata/revision/field_dictionary.csv\")\n",
+    "display(full_dictionary[full_dictionary.field.isin([\"event_time_utc\",\"epoch_start_utc\",\"epoch_end_utc\",\"event_time_status\"])][[\"table_path\",\"field\",\"arrow_type\",\"definition\",\"definition_status\"]].head(8))\n",
+    "croissant=read_json(REPO/\"metadata/revision/croissant.json\")\n",
+    "assert len(croissant[\"recordSet\"])==100\n",
+    "print(\"Croissant recordsets:\",len(croissant[\"recordSet\"]),\"; metadata parsing is separate from data validation.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a369d2a",
+   "metadata": {},
+   "source": [
+    "## 7. Descriptive analysis\n",
+    "\n",
+    "The case payment is 1,000 USDC.e: 750 returned principal plus 250 token gain. The configured request reward is a different quantity. USDC.e has six decimals; gas uses another asset and is not silently deducted.\n",
+    "\n",
+    "The daily table is a bounded full-release inspection: 13,269 typed rows, with source and timed-population counts. A row count describes released groups, not the number of independent participants. Missingness below uses one episode per field as its denominator; it is tutorial-only. The paper map names which broader products support manuscript figures and appendices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e297b5c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stage(\"analyze_data\",\"analyze.py\")\n",
+    "summary=read_json(RUN/\"analysis/summary.json\")\n",
+    "principal=int(summary[\"principal_returned_raw\"]);gain=int(summary[\"realized_payoff_raw\"]);gross=int(summary[\"gross_payout_raw\"])\n",
+    "assert principal+gain==gross==1000000000\n",
+    "display(pd.DataFrame([{\"component\":\"returned principal\",\"raw_units\":principal},{\"component\":\"token gain\",\"raw_units\":gain},{\"component\":\"gross payment\",\"raw_units\":gross}]))\n",
+    "missing=pd.DataFrame([{\"field\":k,\"missing\":int(v is None),\"denominator\":1,\"unit\":\"episode\"} for k,v in episode.items()])\n",
+    "missing.to_csv(RUN/\"analysis/missingness_with_denominators.csv\",index=False)\n",
+    "display(missing[missing.missing==1])\n",
+    "daily=pd.read_parquet(NATIVE/\"data_source/derived/daily_measurements.parquet\")\n",
+    "assert len(daily)==13269\n",
+    "display(daily.groupby(\"branch\").agg(rows=(\"branch\",\"size\"),coverage=(\"coverage_status\",lambda s:\", \".join(sorted(set(s))))))\n",
+    "display(daily.groupby([\"branch\",\"mechanism_version\",\"metric\"])[[\"source_population_count\",\"source_timed_count\"]].max())\n",
+    "display(pd.read_csv(REPO/\"metadata/revision/paper_data_code_map.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c691261",
+   "metadata": {},
+   "source": [
+    "## 8. Validation and reproduction\n",
+    "\n",
+    "Case checks, sample identity checks and full-release validation have different populations. A deterministic rerun compares scientific outputs from the same inputs and code. It does not substitute for another reader's attempt or Google-hosted execution. Source completeness, historical parameter bindings and independent factual truth are not established by these tests.\n",
+    "\n",
+    "The export includes only the public case, native sample outputs and runtime records. It excludes credentials and the full dataset. Source rights remain those in `DATA_LICENSE.md`. The English acceptance guide explains how an independent reader should retain interventions and report discrepancies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24c77aff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stage(\"technical_validation\",\"validate.py\")\n",
+    "validation=read_json(RUN/\"reports/validation.json\")\n",
+    "assert validation[\"passed\"] and validation[\"passed_checks\"]==28\n",
+    "display(pd.DataFrame(validation[\"checks\"]))\n",
+    "display(pd.read_csv(REPO/\"metadata/completion_audit/validation_scope.csv\"))\n",
+    "SECOND=WORK/\"case-repeat\"\n",
+    "command([PYTHON,REPO/\"scripts/run_pipeline.py\",\"--output\",SECOND],REPO)\n",
+    "assert read_json(RUN/\"analysis/summary.json\")==read_json(SECOND/\"analysis/summary.json\")\n",
+    "EVIDENCE=WORK/\"evidence\";EVIDENCE.mkdir()\n",
+    "shutil.copytree(RUN,EVIDENCE/\"case\")\n",
+    "shutil.copytree(SECOND,EVIDENCE/\"case-repeat\")\n",
+    "shutil.copytree(NATIVE/\"processed_data\",EVIDENCE/\"native-sample-results\")\n",
+    "shutil.copy2(WORK/\"commands.log\",EVIDENCE/\"commands.log\")\n",
+    "(EVIDENCE/\"identity.json\").write_text(json.dumps(identity,indent=2))\n",
+    "(EVIDENCE/\"scientific.freeze.txt\").write_text(command([PYTHON,\"-m\",\"pip\",\"freeze\"]))\n",
+    "inventory=[{\"path\":str(p.relative_to(EVIDENCE)),\"bytes\":p.stat().st_size} for p in sorted(EVIDENCE.rglob(\"*\")) if p.is_file()]\n",
+    "display(pd.DataFrame(inventory))\n",
+    "(EVIDENCE/\"inventory.json\").write_text(json.dumps(inventory,indent=2))\n",
+    "export=shutil.make_archive(str(WORK/\"oracle-atlas-evidence\"),\"zip\",EVIDENCE)\n",
+    "display(FileLink(export))\n",
+    "print(\"Case checks and deterministic repeat passed. Independent-reader acceptance remains separate.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "178de01f",
+   "metadata": {},
+   "source": [
+    "### Paper, data and acceptance\n",
+    "\n",
+    "The current manuscript is `paper/working_revision/main.pdf`; Appendix B includes native-to-UTC examples. The dataset's ten products and fixed revision are recorded in `configs/atlas_utc_release.json`. Use `scripts/load_atlas_product.py CONFIG` to list all associated tables, or add `--output DIRECTORY` to retrieve the product's non-shared tables with hash checks. Shared tables require explicit `--include-shared` and subsequent branch/key filtering.\n",
+    "\n",
+    "Cite Oracle4CEG, *Oracle4SD Atlas: eight oracle branches, UMA tasks and daily measurements*, version `atlas-utc-20261029`, Hugging Face (2026), together with the resolved dataset commit. No DOI is assigned. The legacy experimental configuration remains available at the predecessor revision recorded in release metadata.\n",
+    "\n",
+    "For a failed checksum, use the pinned input again and preserve the failure. For an unavailable historical endpoint, retain the acquisition error and use archived replay only for its declared scope. Restart the kernel and run all cells after an interrupted execution. Report reproducibility issues through the Oracle4SD-Atlas GitHub repository, including the evidence ZIP and any intervention; do not include credentials."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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+---
+license: cc-by-4.0
+language:
+- en
+configs:
+- config_name: uma
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/polygon_uma_request_rounds.parquet
+- config_name: chainlink
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/chainlink_staking_v02_events.parquet
+- config_name: tellor
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/tellor_disputes.parquet
+- config_name: flare_ftsov2
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/flare_reward_epochs.parquet
+- config_name: pyth
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/pyth_ois_reward_epochs.parquet
+- config_name: chronicle
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/chronicle_ethereum_events.parquet
+- config_name: redstone_push
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/redstone_ethereum_push_events.parquet
+- config_name: dia
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/native/dia_staking_withdrawals.parquet
+- config_name: uma_prediction
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/prediction/uma_prediction_tasks.parquet
+- config_name: daily_measurements
+  data_files:
+  - split: train
+    path: atlas/releases/atlas-utc-20261029/data/daily/daily_measurements.parquet
+pretty_name: 'Oracle4SD Atlas: eight oracle branches, UMA tasks and daily measurements'
+tags:
+- blockchain
+- oracles
+- economic-measurement
+- time-series
+---
+# Oracle4SD Atlas
+This release contains ten logical data products for studying oracle participation, incentives and accountability. Eight products retain protocol-native observations; the other two provide UMA request-level prediction tasks and daily measurements. The full release has **100 catalogued analysis tables**. Tables shared across products are counted once in that inventory. Additional Parquet files hold supporting metadata, mirrored nested records or retained reproduction inputs; they are listed separately in the package inventory.
+**Version:** `atlas-utc-20261029`. [Processing code](https://github.com/Oracle4CEG/Oracle4SD-Atlas/tree/71c0e19d3ae2b47d763243371f1bfb97750db61d) · [Manuscript](https://github.com/Oracle4CEG/Oracle4SD-Atlas/blob/9d7a227d4895f31a5c2b865d4dd66e3d1ce88b8a/paper/working_revision/main.pdf), [full table catalogue](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/blob/main/atlas/releases/atlas-utc-20261029/metadata/table_catalog.json), [field dictionary](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/blob/main/atlas/releases/atlas-utc-20261029/metadata/field_dictionary.csv), [product membership](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/blob/main/atlas/releases/atlas-utc-20261029/metadata/logical_products.json).
+## Ten datasets
+Each Viewer configuration opens one representative table with a consistent schema. The table count below includes all native and shared tables associated with that product; it is not the number of tables loaded by the Viewer. Use the product manifest to retrieve additional tables. A `train` split is only a loading label here and does not define a modelling partition.
+| Configuration | Product | Preview rows | Associated tables |
+| --- | --- | ---: | ---: |
+| `uma` | UMA | 42,676 | 42 |
+| `chainlink` | Chainlink | 174,890 | 21 |
+| `tellor` | Tellor | 13 | 32 |
+| `flare_ftsov2` | Flare FTSOv2 | 183 | 29 |
+| `pyth` | Pyth | 49 | 24 |
+| `chronicle` | Chronicle | 140,526 | 19 |
+| `redstone_push` | RedStone Push | 614,319 | 18 |
+| `dia` | DIA | 129 | 19 |
+| `uma_prediction` | UMA prediction | 810 | 3 |
+| `daily_measurements` | Daily measurements | 13,269 | 12 |
+UMA, Chainlink, Tellor, Flare FTSOv2 and Pyth are the detailed panels. Chronicle, RedStone Push and DIA are narrower extensions. The Push sample does not cover all RedStone services or AVS economics. Coverage varies by implementation, chain, period and source; first and last observations do not establish uninterrupted collection.
+## Loading data
+```python
+from datasets import load_dataset
+dataset = load_dataset(
+    "Oracle4CEG/OracleEconLab-Oracle-Incentives-v1",
+    "daily_measurements",
+    revision="atlas-utc-20261029",
+    split="train",
+)
+```
+For tables beyond a configuration's preview, download the declared file directly. This avoids combining tables that have different observation units or schemas.
+```python
+from huggingface_hub import hf_hub_download
+import pandas as pd
+path = hf_hub_download(
+    repo_id="Oracle4CEG/OracleEconLab-Oracle-Incentives-v1", repo_type="dataset",
+    revision="atlas-utc-20261029",
+    filename="atlas/releases/atlas-utc-20261029/data/prediction/feature_availability.parquet",
+)
+availability = pd.read_parquet(path)
+```
+The repository's `scripts/load_atlas_product.py` lists product members, downloads selected tables and verifies their SHA-256 hashes. Downloading every physical table requires about 25.5 GB, plus space for caches or analysis outputs.
+## Measurement and reuse
+Daily measurements contain **13,269 rows** grouped by UTC date, protocol branch, mechanism, role, asset and economic stage. They retain both the source population and the subset with supported times. All rows carry a partial-coverage qualification. Missing days are not filled with zero. Escrow exposure covers observed closed requests; signing weights describe epoch policies and are not deposited capital.
+The UMA task contains **810 requests and 16 candidate features**. Its cohort is retrospectively restricted to challenged, linked, settled and transfer-reconciled requests. The label compares the protocol's proposed and resolved integers, including special resolution values; it is not independent factual truth. Four candidate features—bond, configured reward, reward/bond ratio and adapter version—still require historical evidence that binds their values to proposal time. Exclude them from a claim of verified decision-time prediction until that evidence is supplied. Feature-level availability and missingness are published with the task. No model-performance claim is made for this release.
+Native tables preserve source fields and add supported UTC clocks. Common tables retain their earlier fields; join their clock companions by the declared record identifier before selecting time windows. Epoch intervals, snapshots, accruals, claims, payments and applied losses have different meanings. Shared core tables contain multiple branches and overlapping representations: filter the intended branch and choose one observation layer before aggregation.
+Automated checks cover file identity, physical schemas, temporal transformations and specified accounting rules. Human review of historical bindings and an independent reader's Google-hosted Colab run remain pending. Historical acquisition receipts, some block times and the complete cross-chain relay are incomplete. Addresses do not establish independent organizations. See the evidence records for each limitation rather than treating unknown values as zero.
+## Versions and citation
+The previous 15-config experimental release remains available at [its fixed revision](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/tree/356fb1aeac18fb4e6672ca1ea56988dac72c3417). Its files are retained. This release changes the default Viewer to ten products and adds a separate version directory; it does not relabel earlier model outputs as newly validated data.
+Cite: Oracle4CEG. *Oracle4SD Atlas: eight oracle branches, UMA tasks and daily measurements*. Version `atlas-utc-20261029`. Hugging Face, 2026. Record the resolved commit hash with each analysis. No DOI has been assigned to this release.
+Project-created data and metadata use CC BY 4.0. Third-party source rights remain as recorded in [DATA_LICENSE.md](https://huggingface.co/datasets/Oracle4CEG/OracleEconLab-Oracle-Incentives-v1/blob/main/atlas/releases/atlas-utc-20261029/DATA_LICENSE.md). Code licensing is specified in the code repository.

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	@@ -0,0 +1,20 @@

+# Template–Atlas completion comparison
+The comparison uses Template `2ca64c50c43e189aa9cfc2cacd03520b71119301` and Atlas `1dc0dfc3bc919e64f43a110bf29b75ef0e34459c`, matching the supplied completion audit. The Template remains unchanged. This Atlas revision implements its fourteen folder roles and eight teaching stages with project-specific artifacts.
+The [30-row response](../metadata/completion_audit/response.csv) uses the attachment's G01–G30 identifiers. It is separate from the manuscript revision log and the older remaining-work numbering. “Implemented” describes the supplied artifact, not independent or hosted acceptance. Local execution evidence and published data checks are retained separately in `reports/publication_20261029`.
+| Template output | Completed Atlas replacement |
+|---|---|
+| Project README | Root README navigation and eight-branch source-to-output table |
+| Source metadata | Case register, acquisition profiles, raw locator profiles and full-source register |
+| Queried data | Case receipts plus bounded raw/native samples for eight branches |
+| Processing description | Tutorial transformation table and executable case/native stages |
+| Dataset dictionary | Case dictionary, physical-table catalogue, full dictionary with review status |
+| Descriptive outputs | Case accounting/missingness, daily measurements and paper map |
+| Quality assessment | Validation scope matrix and machine-readable check results |
+| Reader handoff | English acceptance guide, manuscript, fixed dataset configuration and pending gates |
+The blank templates are replaced by these completed documents. Existing production collectors, reconstruction code and the small case wrappers serve different scopes; a successful case run does not establish full-history collection.
+Five audit items still contain external or semantic work: historical collection provenance (G17), deeper native-field definitions (G20), independent-reader reproduction (G25), Google-hosted execution (G26), and a named reviewer/agreed checkpoint (G28). These are not converted to passes by publishing data.

atlas/releases/atlas-utc-20261029/code/load_core_with_utc.py ADDED Viewed

	@@ -0,0 +1,26 @@

+"""Create DuckDB views joining unchanged core records to validated UTC clocks."""
+from pathlib import Path
+import argparse,json
+import duckdb
+TABLE_KEYS = {
+    'accountability_events':'accountability_event_id',
+    'sample_b_observable_accountability':'accountability_event_id',
+    'sample_c_strict_honesty_events':'accountability_event_id',
+    'economic_semantics_events':'evidence_id',
+    'realized_reward_slash_events':'evidence_id',
+}
+def register_views(connection, deposit):
+    root=Path(deposit).resolve()
+    connection.execute("SET TimeZone='UTC'")
+    for name,key in TABLE_KEYS.items():
+        parent=str(root/'data/core'/(name+'.parquet')).replace("'","''")
+        clocks=str(root/'data/core_clocks'/(name+'_clocks.parquet')).replace("'","''")
+        connection.execute(f'CREATE OR REPLACE VIEW "{name}_utc" AS SELECT p.*, c.* EXCLUDE ({key}) FROM read_parquet(\'{parent}\') p LEFT JOIN read_parquet(\'{clocks}\') c ON p.{key}=c.{key}')
+    return [name+'_utc' for name in TABLE_KEYS]
+if __name__=='__main__':
+    p=argparse.ArgumentParser();p.add_argument('--deposit',required=True);p.add_argument('--database',default=':memory:');a=p.parse_args()
+    c=duckdb.connect(a.database,config={'threads':2,'memory_limit':'4GB'})
+    print(json.dumps({'views':register_views(c,a.deposit),'timezone':'UTC'}))

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