license: cc-by-nc-4.0
language:
- en
pretty_name: MatrixNorm BVA AMA-Era 170K
size_categories:
- 100K<n<1M
annotations_creators:
- machine-generated
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- original
tags:
- legal
- veterans
- bva
- board-of-veterans-appeals
- administrative-law
- legal-ai
- public-records
- evidence-extraction
- issue-level-outcomes
- citations
- matrixnorm
task_categories:
- text-classification
- token-classification
- feature-extraction
- text-retrieval
MatrixNorm BVA AMA-Era 170K
MatrixNorm BVA AMA-Era 170K is a normalized legal-AI dataset built from public Board of Veterans' Appeals (BVA) decision records from the AMA era.
This dataset converts messy public veterans-law decisions into structured, AI-ready legal records with extracted issues, outcomes, conditions, citations, judges, appeal-lane signals, metadata, and reasoning fields.
Current release: ~181K rows — but ≈108K unique decisions (this file contains duplicate rows; see the data-quality note below). A cleaned, de-duplicated version is in progress.
This dataset is part of the broader MatrixNorm project: turning messy public records into structured, AI-ready evidence graphs.
⚠️ Data-quality note — duplicate rows (cleaned version coming)
This file currently contains duplicate rows. It has 181,024 rows but only 107,817 unique decisions — roughly 73,000 rows are exact, byte-for-byte duplicates. The file was concatenated from per-year source CSVs that overlapped on 2021 and 2022, so those years are repeated ~2–3×. De-duplicating on the decision id collapses the file to its true size with no loss (the extra rows are identical copies).
Unique decisions by year (after de-duplication):
| Year | Unique decisions |
|---|---|
| 2019 | 3,905 |
| 2020 | 19,476 |
| 2021 | 20,638 |
| 2022 | 26,287 |
| 2023 | 37,512 |
| Total (2019–2023) | 107,817 |
If you train on this file as-is: de-duplicate on the decision id first, and use a grouped split by decision id so duplicate (and multi-issue) rows don't leak across train/test.
📌 An updated version is coming. A cleaned, de-duplicated release will supersede this file. Watch this repo for the update.
Overview
Board of Veterans' Appeals decisions contain valuable legal and administrative reasoning, but they are difficult to use directly because the source records are long, inconsistent, and mostly unstructured.
MatrixNorm transforms those public records into structured legal data designed for research, retrieval, analytics, and legal-AI workflows.
The goal is to make AMA-era BVA decisions easier to use for:
- legal AI research
- veterans-law analytics
- issue-level outcome modeling
- citation extraction
- grant / deny / remand classification
- condition extraction
- appeal-lane analysis
- judge-level pattern analysis
- reasoning-pattern analysis
- public-record normalization benchmarks
Why this matters
Most legal datasets are either raw text collections or broad case-law corpora.
This dataset focuses on a specific, high-impact administrative-law domain: veterans benefits appeals.
Instead of only providing full decision text, MatrixNorm extracts structured fields from BVA decisions, making the data more useful for search, analysis, model training, evaluation, and research.
Dataset status
This release contains approximately:
- 170K normalized AMA-era BVA records
- structured issue and outcome fields
- claimed-condition extraction
- judge and decision metadata
- citation extraction
- appeal-lane and procedural signals
- reasoning-pattern fields
A cleaned, de-duplicated version of this dataset is in progress (see the data-quality note above).
Fields
Exact columns may vary by release file, but fields may include:
| Field | Description |
|---|---|
decision_id |
Unique decision or source identifier |
decision_date |
Date of the BVA decision when available |
docket_number |
Docket or appeal identifier when available |
appeal_lane |
AMA lane or procedural signal when extracted |
judge |
Veterans Law Judge name when extracted |
issue_text |
Raw or cleaned issue statement |
condition |
Normalized claimed condition or disability |
outcome |
Issue-level outcome label |
outcome_group |
Grant / deny / remand / dismiss style grouping |
citations |
Extracted legal citations |
statutes |
Extracted statutory references |
regulations |
Extracted regulatory references |
evidence_terms |
Extracted evidence or medical/legal signal terms |
reasoning_pattern |
Normalized reasoning or decision pattern |
source_text |
Source decision text or excerpt, depending on release |
metadata |
Additional extracted decision metadata |
Suggested use cases
Legal AI research
Use the dataset to study administrative-law decision patterns in veterans benefits appeals.
Possible tasks:
- issue classification
- outcome classification
- condition extraction
- citation extraction
- procedural-lane analysis
- decision-pattern clustering
- legal retrieval
- metadata enrichment
Veterans-law analytics
Potential research questions:
- Which claimed conditions appear most often?
- Which issues are most frequently remanded?
- Which citations appear most often in grants, denials, and remands?
- How do AMA appeal-lane signals relate to outcomes?
- Which reasoning patterns repeat across decisions?
- What conditions produce the highest remand frequency?
Legal data normalization
This dataset can also be used to test extraction and normalization pipelines for messy public legal records.
Possible evaluation tasks:
- issue segmentation
- citation parsing
- condition normalization
- judge extraction
- date extraction
- procedural metadata extraction
- outcome labeling
Data source
The underlying decisions come from public Board of Veterans' Appeals records.
MatrixNorm adds value by normalizing, structuring, extracting, labeling, and organizing the public decision content into AI-ready records.
Raw BVA decision text originates from public U.S. government records. MatrixNorm's normalized schema, extracted fields, annotations, metadata, issue labels, citation parsing, reasoning-pattern fields, and dataset organization are released under the license listed below.
License
This dataset is released under CC BY-NC 4.0.
You may use it for research, education, nonprofit, and other non-commercial purposes with attribution.
Commercial use is not permitted under this public license. For commercial licensing, partnerships, or enterprise access, contact the dataset maintainer.
Suggested attribution:
MatrixNorm BVA AMA-Era 170K by William T. Miller / MatrixNorm. Normalized public Board of Veterans' Appeals records for legal-AI research. Released under CC BY-NC 4.0.
Limitations
This dataset is automatically normalized and may contain extraction errors.
Known limitations may include:
- imperfect issue segmentation
- imperfect condition normalization
- missing or inconsistent judge extraction
- citation parsing errors
- mixed outcomes inside a single decision
- legacy appeal records appearing inside AMA-era date ranges
- incomplete appeal-lane detection
- OCR or source-formatting artifacts
- labels that may require human review for high-stakes use
- duplicate rows in the current file (≈73K exact-duplicate rows; ~108K unique decisions) — see the data-quality note near the top; a cleaned version is coming
Do not treat this dataset as legal advice. Do not use model outputs based on this dataset as a substitute for an attorney, accredited representative, Veterans Service Organization, or official VA decision review.
Project direction
MatrixNorm is building a network of normalized public-record datasets across legal, health, safety, food, environmental, and government domains.
The goal is to create structured evidence data for:
- AI training
- AI evaluation
- retrieval systems
- risk analysis
- public-interest research
- domain-specific legal and administrative analytics
MatrixNorm turns messy public records into usable AI infrastructure.
Maintainer
William T.L Miller — MatrixNorm
Hugging Face: williamTLmiller
Commercial licensing, collaborations, and research partnerships are available by request.