---
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](https://huggingface.co/williamTLmiller)

Commercial licensing, collaborations, and research partnerships are available by
request.